{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Article\n",
    "\n",
    "The data here is taken form the Data Hackathon3.x - http://datahack.analyticsvidhya.com/contest/data-hackathon-3x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data:\n",
    "\n",
    "The data has gone through following pre-processing:\n",
    "1. City variable dropped because of too many categories\n",
    "2. DOB converted to Age | DOB dropped\n",
    "3. EMI_Loan_Submitted_Missing created which is 1 if EMI_Loan_Submitted was missing else 0 | EMI_Loan_Submitted dropped\n",
    "4. EmployerName dropped because of too many categories\n",
    "5. Existing_EMI imputed with 0 (median) - 111 values were missing\n",
    "6. Interest_Rate_Missing created which is 1 if Interest_Rate was missing else 0 | Interest_Rate dropped \n",
    "7. Lead_Creation_Date dropped because made little intuitive impact on outcome\n",
    "8. Loan_Amount_Applied, Loan_Tenure_Applied imputed with missing\n",
    "9. Loan_Amount_Submitted_Missing created which is 1 if Loan_Amount_Submitted was missing else 0 | Loan_Amount_Submitted dropped \n",
    "10. Loan_Tenure_Submitted_Missing created which is 1 if Loan_Tenure_Submitted was missing else 0 | Loan_Tenure_Submitted dropped \n",
    "11. LoggedIn, Salary_Account removed\n",
    "12. Processing_Fee_Missing created which is 1 if Processing_Fee was missing else 0 | Processing_Fee dropped\n",
    "13. Source - top 2 kept as is and all others combined into different category\n",
    "14. Numerical and One-Hot-Coding performed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_modified.csv')\n",
    "test = pd.read_csv('test_modified.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((87020, 51), (37717, 50))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target='Disbursed'\n",
    "IDcol = 'ID'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    85747\n",
       "1     1273\n",
       "Name: Disbursed, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Disbursed'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a function for modeling and cross-validation\n",
    "\n",
    "This function will do the following:\n",
    "1. fit the model\n",
    "2. determine training accuracy\n",
    "3. determine training AUC\n",
    "4. determine testing AUC\n",
    "5. update n_estimators with cv function of xgboost package\n",
    "6. plot Feature Importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_results = pd.read_csv('test_results.csv')\n",
    "def modelfit(alg, dtrain, dtest, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)\n",
    "        xgtest = xgb.DMatrix(dtest[predictors].values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrain[predictors], dtrain['Disbursed'],eval_metric='auc')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrain[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]\n",
    "        \n",
    "    #Print model report:\n",
    "    print \"\\nModel Report\"\n",
    "    print \"Accuracy : %.4g\" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions)\n",
    "    print \"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob)\n",
    "    \n",
    "#     Predict on testing data:\n",
    "    dtest['predprob'] = alg.predict_proba(dtest[predictors])[:,1]\n",
    "    results = test_results.merge(dtest[['ID','predprob']], on='ID')\n",
    "    print 'AUC Score (Test): %f' % metrics.roc_auc_score(results['Disbursed'], results['predprob'])\n",
    "                \n",
    "    feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n",
    "    feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "    plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1- Find the number of estimators for a high learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until cv error hasn't decreased in 50 rounds.\n",
      "Stopping. Best iteration:\n",
      "[140] cv-mean:0.843638\tcv-std:0.0141274405467\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.899857\n",
      "AUC Score (Test): 0.847934\n"
     ]
    },
    {
     "data": {
      "image/png": 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tZraMmqr16M3MxoUL0mZmNlKuzmJm08VYFqQlvULSbyX9TtLH+l/CZIXU64qt\nM+2pGFtn2nXF1pn2VIytM+26YutMe3SxSxbE5zGqQvggr6RPTk4WnndcYutM2995asTWmXZdsWNX\nkJY0A/hv4OXAM4F9JW3W31ImK+Sgrtg6056KsXWmXVdsnWlPxdg6064rts60p0bs0lfDD6VoIbxb\nbL/VWXbaaafS9dH7iW2NrxJbJr7ZVCwk1Zn2VIytM20XpBfbFrgpIm6NiIXAScAeNefJzMystCqF\n8CqxS8dXia325+FTn/pU6T8P/cS2xvcba9aPcSxIrw/c1vT+9jzNzMzMpoip+uehWyG+3yv4o/rz\nUOcfjyrrazoYuwFZJO0FvDwi3pnfvxnYNiLe3zTPeGXazMzMzKatTgOyzBp1Rgq4A9iw6f0Gedrj\nOn0ZMzMzM7NRGceqHb8GNpW0kaTlgX2AM2rOk5mZmZnZEsbuinREPCbpX4FzSQX970TEDTVny8zM\nzMxsCWNXR9rMzMzMbCoYx6odZmZmZmZjzwVpMzMzM7MSXJA2GxFJq0lare589EPSGlXzLGntknGr\nSXqupDWqpG/DI2nNNo/l6s5XL4281pyH59SZvk1fPneO1rSpIy3p6cD/A9aNiGdJ2hLYPSI+0yPu\n66Re2ttq7r+6Tey6wGeBJ0fErpK2AF4QEd8pkN9re6S7ZYe4tSPinqb3byaNBnkdcHT0uUElXRAR\nO/cx/5V0z3fHHwdJqwOHAHsC6+Tl3A2cDnw+Iu4fRmw3ks6KiF27fL5aTncD4KyI+H7TZ0dFxIE9\nlr8B8HnSkPd/AwSsTGpM+/GI+GOHuKcAXyQNRnQW8MU80ieSfhQRe3ZJczPgK8Ai4P3AJ0nr7XfA\nfr0a70p6cs7zHsCqLO5+8hjgPxv56BC7K3BUjnkf8D1gRWCFnPZPu8R+D/hARNwj6eXA0TnPTwM+\nHBGndMt3l+VeGxH/NMzYvH++gsWDR90BnNNrv6yynauk27KMD7aZ/Ffg8oi4qkfsfOApwH2kffuJ\nwF3AAuAdEXF50Xzk5b0sIs7rJ6YlvuPxLGlD4AvAS4D7c35XAy4ADo6I+QXT+FqbyX8FLouI0zvE\ntJ4XRTp37Ub6Hb6iSNptlttzfUn6Z2BBRNwo6UXAC4AbIuJ/e8SVPo9I2j8ijsmvNwDmAM8Frgfe\nGhG/K/DdVgOeFBF/aJm+ZURcUyBepN/G5mPj0qK/kZLa/fY3jovriiyjZXlXRcRWfcy/MbA1cH1E\n/LbHvANv0kNBAAAgAElEQVQ9d0r6bER8vM+Y17aZ/Ffg2oi4u838lfYRSX8BfgicCFzQT9lH0u7A\nuRHxf0Vjui5vGhWkLwQ+AnwzIrbO066LiGf1iNuv2+cRMadL7FnAscC/R8SzJc0Criz4w7tRfvne\n/Hx8fn5TTvfgDnFXNAqrkj4BbA98H3g1cHtE/FuXNFtPPgKeDtyY02xbeG9Zxib55buBmS35fiwi\nPtYl9hzSj9aciLgrT5sA9gNeEhG7DCm2U+FewI8jYr0usacCNwGXAPsDC4E3RsTfm7dFl/iLSQXL\nuU0FpOWANwAHRsQLO8SdB5ya0z2AdILZLSLulXRlYx/vEPszUuFsVVKB+GPAyaR95AMR8ZIeeb4A\nODwiJvPJcXvgE6Q/FOs0BkvqEHsVsC+pQPVj4FURcYmkzYETevzRerzQKukXpPU8P1/R/mlEPLtL\nbLuTOKRt/D8R8aRhxOb4t5CGTjuXxX86NgBeBnwqIo7rEltlO5dOt2U53we2Ac7Mk14NXAPMBk6J\niC90iT0a+EFEnJPf7wLsRTovHhkRzy+Sh6bl/TEiNuwxT6njWdIvga/m/D6Wp80EXk86LrYrmMdv\nAZsBjcLJXsAtwFrAzRHxgTYxi0jb+O9Nk7fL06Kfixkty+26viR9lVSYnAWcQ/oTcRawA+m36iNd\nYkufR1p+p+YC5wPfJv05/9cC56C9SdvqbmA5UsHq163L7hK/C+m8exNLHhubks6753aLz8s4CXge\n6TwG8ErScbEx6Vz25TYxu3daHOlC1zpd0nv8j7OkPUjffxJ4IfC5iPhul9gq587WP4YC/gU4Drpf\nTGxZzv+S/qTNy5N2BC4nra/DI+L4lvmr7iM3Al8n/d7MBn4AnBgRlxTI6yPAQ6Rj4UTSxYfHinzP\ntiJiWjyAX+fnK5umXTXuaTbHNk27osj8wBXAKvn1cqR/ft3SOoN0hXAzYKO8892WX2/UZ76XymO3\nfOfPbyzz2QBiHyMVwue1eTzSI/aqlvf/DlxM+tHs+n3z/DeV/Kw13TcDvwE2KbCem/eR3/ezjfI8\nV7e8v7zp9W+L7hfAbd2+U5vY3wCr5dcXATOaP+sRuxD4LqkA1/p4cFixjf0PeGKb6WsAv+tz/+pn\nO5dOt2X+nwGrNr1fFbgQWIl0Naxb7FLnHOCabts7n4faPc4EHiqQ31LHc9ljsc28lwAzm97PAn5J\nurDQdn2RCtsXArs2TbulYHql11felxp3we4DVs7TlwOu6xFb+jzSch5oPZ8s9ZvXJv4qYL38elvg\nt8Br+oi/AZjdZvrGpKvxRdb7hcATmt4/IU9buct2Xkj6jT2+zaPXeah5ff8C2Di/Xrt1HXbYzmXP\nnbflPL+FdGFqP+DPjdd9HBfnkGoENN6vm6et2W5fG8A+0hy/IfBRUpnoZuCzvdY16Tz5DuCnpDto\n/wPsUPT7Nj/Grh/pCu7JV0sDQNLrgDt7BUnqOthLRHT6hwnwkKS1mtLcjnQrox+S9KKIuDi/eSHd\n666vJGnrPM9yEfFQzudCSV3/UUXE7pJeA3wL+FJEnCFpYUTc2meeAWZK2i7yvz9Jzyf9kHRzq6SP\nkq4qL8hx6wJvJR3Mw4q9AXhXRNzU+oGkXrErSJoREYsAIuI/Jd1BLnz0iAW4Kv/jn9OUz6fkfF/d\nJW45SStGvvUUEd+TdBfpxLRKjzSbt8N/tXy2fIE8/1mpytA84LXAfHj8VmmvdhX3S3oX6Zb5fZL+\nDZgLvJRUtaWbTwHzJH2D9GfllHx87gSc3SP2GtI+vdQtV0kvHWIspIJKtJm+KH/WTZXtXCXdZuuw\n5JXShaQfxEck/b1DTMOdkj4GnJTfvwFYkK/0LuoQsz3pD0Pr/tC4Fd9L2eP5cklHsfSxuB/ph7Wo\nNUjHfuNcvwqwZqQxENqur4g4Nd9V+7Sk/YEP0aWKXIsq6ysiIvIVcZrSXETvY7nKeWSDfN4TsLak\n5WJxlbAidehnRsSdABFxqaSdgB8rVYUqst5mAbe3mX5HwfQhFQQfaXr/d9Jx8XCX4+Ja0tXj37R+\nUOC3pvl7LR8RtwBEqq7R6VhqqHLu3AL4NKmK2Icj4k+SDo0ud+M7eErjtzm7O0/7i6R21QGr7iOP\nn+MiVZH8AvAFpSpJb+gRGxFxH6kKzNH57vbewOclbRARTymQ/uOmU0H6vaQC4ma5oHML6eTTywtI\nJ9UTgV/R3w/QB0lXBjbJt/CfBLyun0yTbuceo1TXEVLdvf27zH8ni09q90haLyLuzAX6R3slFhGn\nSTqXdEI/gGIFq3beDhwracX8/pEe+Ya0cx8MXCipcYtrAWkd7j3E2MPo/KPxvh6xZwI7k247ARAR\n382Fna/3iIW0D74TOILFdfVuz8vteFuVdIvr+aQrII10z5f0etIJo5tvSFo1Iv4WEUc1JkratPl7\ndLE/8CXS+r4K+Nc8fU1S9Y5u9iNVA1kE7EK67XYOcCvp339HETFX0hV5vqeTzk/bkW7XndMj3Q8A\nD3T47DVDjAX4T+CKfFw1fiw3JFWx+HSP2CrbuUq6zU4AfiWpUb93N+D7klYh1Vfs5o2k6iU/yu8v\nztNm0vm4vAR4OCIubP0g367t5TDKHc9vIZ1vP8XSx2LPdi1NvkD6gzxJ+r34Z+CzeX11PL4i4m/A\nv+ULIXMo9kccqq2v/5X0c1I7hW8DcyVdQqra8bMesVXOI83ntstI3/W+XGApMlLxg5I2iVw/Ov/G\n7Ujaz55ZIP4Y4Ne5ekbzn6Z9KL6tTwZ+Kamxb+8OnJy3c6f1/kE6XzB4fY/0ni3pAdI+tULTb/vy\n9LhIVeXcGREPAh+Q9FzghFxFo0xHFJOSfsySVZ4m8/pq12aj6j4yr93ESPXJP9UjdolyXqTqol8D\nvqbF1W6LK3MZe5wfpKsDT+hj/pmkf2JzSFclPgM8s4/4WaQD+1mkK8Rl8706sHrBeQVs2OZ7rFww\n9in59bOBd1dc32sBaw14G+63LMXm+I+WjDukQpqlY+tMexxjSVcp9yFdafxQfr1GlfVbJO1BpUuq\nC3pQfmwzqHzX+Sh7TBbZR4D1SHU59yA1Nu83DZFvxfebdom0XgBsl19vAnyY9CdnxoCWP/BjKv82\nbdpm+nLAmwoue3PSxYCv58fBwBZ95m+7pmNruwFuk8Lne1J7kxeMYlvl/fK9wPdKLFukC4lfyY/X\nkdvh1bV/dYoHdhzUtoyIadXY8ImkKw6zabrSHgUryudlrEC6gvZFUmOd/+4xf1+tVDsso1TPHxpB\nTwQ9lvEk0p+O9SPi1Tnf20aXBhF9LLtnY5LpFFslfirmeVmMrapivn8ZES/oMc9M0q3s5nNn2x5l\nWuKeTiqYzW6JLdV4bpCGeUxJWp/UtqT5O/e6wjuQtLvE9tzOwzBVj6mc/qkRsVeXz0W609y8nf80\ngHRr+d51r+8y6vydKmo6Ve34CekW2LV0rpvXVi5Av4rFrT+/BpxWIPQAOrRSlbRUK9UOvkvu+SO/\n/x3pllKv209XSHpe5JbMfaoS2/Bd0i3hRi8dN5Hy/d0Ky2zop3rNdIitEj8V87zMxA7iT2vZtJus\n2O1DSe8jVc9YQGrI16h73bMXH9Jt3P8hVRso3+p9cV4Gtb5gSMeUpCNIVc1+w+LfmqB3VYnKaffQ\ndTsvldB47Jt1HlMAT+2SzoHA4cC9LHlcbDGAdPv63nVtq37SzRcWjyC1uVB+RERUHTthpL9TZdb1\ndCpIrxgR7fpD7UrScaRqGT8hXYXup3/IWcDmsWTjt+NIdR5/xuKu4bpZO1L9pkMAIuJR9Wg0mD0f\neJOkW0nduDR22iI/flViG9aJiO9L+kjO98ICDSKKqnKbZCrGVomfinmeVrEd7kxBOq4mKqTXM+0B\nxR4EPCMi7i2x7Ecj4v/1EzCi9QXDO6b2JK2vXg0xh5F2X7FTdd8ck33kg6Tf9z8PML2O6da1rQaY\n7hdIXXd2HaeghIH/Tg16XU+ngvTxkt5B6vPx8RNcRPylR9ybSYXJg4D3pzs5QLF/U/22Um2nbM8f\nLy+4/EHHNjykNDJYI9/Po3ODrX5NmSuOA4qtEj8V8zzdYk8m3Z1pd8Lv6yphibQH4Tb6722o4cx8\n5e40ip93R7G+YHjH1M2kurrDKEgPejuP+75Z5zHVy+1Ar/JDWe2+d13balDpLhhCIRqG8zs10HU9\nnQrS/yDVbf53Fq+coMutG4CIqDJMer+tVNsp1fNH5C7rcg8WfW34KrFNPkxq7f5UpcFw1qf/Hks6\nuXgZi4U0QlMZpUb7G0BsnWmPW2zV7vOqpF1EkYLhZG6t31wYbu3yrJ398nNzC/xe591RrC8of0z2\nWtcPk3rt+ClLrq/C7XEqpN1Nu+087vtmnccUdD82fg9ckH/jm7dzu5Et+9XufF/XthpUupdJOpnU\ns0rz+ir729YwjN+pga7r6dTY8GZSY7d7es68ZNzOEXFBfr1x5L4b8/vXdtsJckOE1wIvzpPuI/Uz\n+d5OMR2WMwt4BumgvjG6DMHcFLM78GXgyaQr4RuROprv2TVQldiW5SxPahktUgf1/ygYV7phaNnG\nmXXG5vhNgW8AE5FGwdySNOrf54rEtyzrPyLi8H7jqsaOIm2l4W03II3GNb9p+uPDyY5bupK2B25t\n1zhP0jYRcdkQ054JnB8RO3WZ51ndqqxJOrTd9Ijo1YVUKVXXV9O8lRuYNy2r8H6tDqPhRo9+d+vY\nznXum12WWeR4HMg+UiAvu0SHUQ4lte1CMiI+WWC5fZ/vh/Wde63vAR6Px7aZHBHRsVvcYexfOX4k\n3/lxMcAuQOp8kIbJ7dn9W5u4K9q9bve+Q/zWpCvh80mNDv+1YLo75+fXtnsUiL+a1PXclfn9TsB3\nCqZdJXaH/Lx7u0fBZfyC1Bf221g8ktJ+BWPPInXddHV+P4seIzrWHZvnnyQN9dpY56LHiFNdlvXH\nMnFVY4edNumPys9IQ+P+AXhf02c9j8VxS7dNOu26YaqcNmlkrkJdZw7wu1Q6f5VdXy2flz6P9LuP\nDOC7jPV2Hta+Oex13WkfIXU6cE2nxzC3dU5/YOf7ot952Ou733QLLG9o591Rf+fpVLXjIdLttnn0\nd7tNHV63e58mpi6f9s2Pe0j1bRRdrha0sQNpmNvd2nwW9L7VvzAi7pU0Q2nkvXmSvlow7SqxLyMN\nHtGuc/mgWEfqpRqGZmUbZ9YZC2ko91806uBHRHSrR6/UMX/bj0hDN3dUJbbmtHcDts7r9jDSoCBP\njYh/o0f1hLrS7dPrgdYrUoNI+2/AtZLOI50Hgd7nPklfjYgPSDqTNnUFo/uorlXPX0W0W1/N+jqP\nDOC4mBsRe0u6liXXV5HG2rVt54IGum9WXdd96LSPvDo/N+4ONxr+v6nXAiV9OSI+JOk02h8XnRqq\nNevrfN+npb7ziNZ323Ut6aMR8QVJX6f9+uq0f1Y6Jur8zq2mU0H6RyweXasf0eF1u/cNvwV+Drw6\nIn4PoDQUcvFEIw7Nz2/rJ67J/ZJWzfk4QdLdNJ1chxUbEZ/Iz/9SIs8NZRuGQrVh2euKBbhX0sZN\n8XsCd3WZ/37gebFkY1ZybK+hZqvE1pn2rIh4FCAi7pe0G/AtSafQewTOutLtR7sfh0Gk/UPKFVwb\nhYsv9Rs4gPNXEb1+TPs9j1Q9Lg7Kz6/uOld7dW7nIga9b1Zd10W13UdicTugl0XE1k0fHaw0AuDB\nXZZ5cn7uOo5ED/2e7/vR7juPYn13Oh4bDQz7rXpS9Zio8zsvYdoUpCNijlKd3afnSYXqGpMay51B\nWmGN1+T3G3eIeS1pBLF5ks4GTqLPK1eSul5JiQ4NfSR9gzSc+R6kYbk/QPqXvTqpz8tuaZaObVpG\n16sfUawhRqmGoVmVYdnrioU0zPZ3SEPY30oa6n2fLvMfR6q7vtRJAvh+j7SqxNaZ9h8k7RB5KOSI\neAw4QNJnSA15xzHdfrT7Y1457XzuW4k02mmRIbYbcZfn5wsb0yStQep56Joiy5B0EKkf/AeBo4Hn\nAAdHh3qnferVgKff80il4yIi7swv7wEeiYhF+e7kZqSqX93Utp0LGvS+WfUcVFSvfUSSXhQRF+c3\nL6TH8NcRcWl+/mnTQlYnDT52fcF89Xu+70e77zyK9d12XUfEmfn58TYCkmYAq0ZEt568qh4TtX3n\nVtOpseGOpGG+55MKtU8h1Zfr2km+pB26fd78I9MmdhVSoXRfYGfShj2tyI+IOjTwaUq3bUOf/MO1\nD2mI2rnAiRFxZa/0qsY2LaNtA4ymfBdpiFGqYWhTfN+NM+uObVrG6qTjrmevLkr3BTeIiL7/XVeJ\nrSvtXEiAVI3mtpbP1o+IO8Yx3T7SubLl6thA0s5Xcr4ELB8RG0vaCji8R9WM5vhJUhuHWaQBpe4G\nLi5SbULS1ZEaU70ceDfwCeD4GMBIYu3WV8vnfZ9Hqh4XeRmXA9uThme/GPg18I+I6FhtYBy2c49l\nD3zfHMS67qXAPvJc4BjSxSJIVzH3j4grCiz7p8BrgJnAFaSu8C6IiI90DVxyGYXP930ss+13Hvb6\nLrCuv086BzxGOiZWA46MiC92mH8Qx0St3/lxMcDK43U+SD8Az2h6/3Tg8gEu/9Qen68BvJPU+nQU\n33cj0qiCV5KqmvwH8PRhxw4o76UahubY9wJPbFnvB45zbNP8/wVcCvyK1GvKGgXiCjdoHGRsnWlP\nxdiCy//4kPJ9OamgcGXTtOv6iG80iHo7aVAqKNggqzEfcCTwmublDXN95c/LNjCvelxckZ/fB3w0\nv75q2GlX3c5l1/VUPaZa5ludPhtqNh0XBwCfzq+LHhelzvd1bquq67pxDJDucn+Z1Nd6z/VV5+9U\n1e/ceFTpQ3ncLBdNt7si4nekDTkovfqjvi8ivhURL+lnoZKeKulMSX+WdLek0yX1rOIQEbdGxBGR\n/i3tS/rnXKgz9CqxTfmeLek0SXflx6mSZhcMbzQM/aakrzUeBWPfEU3/7iPiPuAdYx4LqfrPg6ST\nzJtJg9ec3DUiuUJpsJsyqsTWmfZUjG1L0n80XkfEZ4eU9sKIaK2v388oo7MkrUfqlebHfaZ9uaRz\ngVcC50h6Qp9pL6GP9QXlzyNVt7MkvYB0LP9vnjazYGyd23kJI9o36zymkLSupO8AJ0XEXyVtIemA\ngknNkvQkUoOzM/vMZtnzfVsj2lZV0gVYTtJypJE/z4h0x7ZIlYc6f6eW0ud3TjG51D3lSTqGdFL5\nXp70JmBmdOnDsM/lXxEDuF3ZZrmXkPqbPDFP2ofUDczze8TNAnbN87+E1N3OiRFxeoE0S8c2LeOX\nwLdIowMBvBF4V0S8oEDsfu2mR49+WHPstcCWkXdcpf5Vr4li/WfXEpvnvy4intVrWpu43wKbAn0P\n514lts60p2Jsl2X+MSI2HHK+v0PqGu1gUt3C95MuLLy7YB5fD3wSuCgiDsx/5L8YET3rKSrVhdwK\nuDlSg6E1SbdaC9WxbrO8Qusrz1vqPDKA42IH4EOk6i9H5PX1gSjWD35t27nN8kaxb9Z2TOV5zyLV\n4f/3SFWQZpGuNP9Tgdh9SHdrL4qId+bt/JWI2KNAbKnzfZflDX1bVUk3z/t+0p3uq4FXARsC34uI\n7YeZ5zq/8+Mx06ggvQLp9ntjcJSfA0dFxECGcR1iQfqa1g2uXO+ww/wvI11FfiXpttFJwOkR0bPX\njSqxVfPdNM9M4LjoUp+wR/yXSAfoN/OkdwG3RcSHxjU2xx8J/DwifpDfvxbYPlJXP93iNmo3PXKr\n9GHF1pn2VItVj26YIqJno+6K+V6Z1OBul5zmOaRb0f/XK7YqSS8i3dJ9SNKbSY0Nj+yW7wGtr9Ln\nkarHRcuyijSqGkjaZbbzGOybtR1TeTm/jojnqamuq6SrImKrIvFllTnf17WtBrWuOyz78Z45uswz\n8t+pQX/n6VSQXgX4v0gtPxsn2hUi4uEBLb9YpfP+l3sEaUTEk0i3Qd5Aql/1RVi6KydJF5BapJ4a\nqXpBP2mVjm2zrM+TWq8353tt4PM53x1/WCRdRBrQodBIiC2xM0h10RvDeJ4HfLux3ccxNsffR6qn\n12iguByLu8+LiFizR/wSw7lHmxGZhhFbZ9pTJVbSH+nSDVNEPGVYaQ+CpC8AnyH15HM2sCXwbxHx\nva6BKfYa4Nk55rvAt4G9I6JjI+5Bra8q55EcX2pdq89GVYNMu1/jsm/WdUwpNaTdCzgvIp6j1G3p\nEd32z6bYz5H6EH6YVIVnK9Jx0bNHiDLn+7q21QDXdXMPPt8mDVZXuAefUf5ODXJdNxKbFg/gEtKV\ngcb7VYFfDHD5uwwp37d0edxc93rtku/bujx6jSR3HOkH6JOkbuU+CHywQJozgRNK5reW2JZldHx0\nidsduIl0y+oWUvWlQiNkVYmtM+2pFksqhG7b4bMjhpU2qd7mGZ0efWznRiOh15C67FqdPIJngdhG\nw7v/AA5onjbM9ZXnLXseqXpclGpUVcd2rmvfrBo7wH3kOaSeVf6an39HqqLXz3bek1RAXLOP46Lv\n832N55FBrevGqL8vJ/V3/sxe54Kq+1fd3/nxmH4DxvVBm1bT7aZ1iW83pOjPga8Aa9X9/abTAzi0\n3aNg7EWk7p/KpFtLbI4/mXxLts+4WoaCrzPtKRorUv/LZfePvtMmjS64A6nHjJNJI4XtRrrr9JU+\n0r4uP38beEUjPwVjLwQOIRVQJkh99PZsRV91feVllDqPDOC4+A2p8HwKsEOf62vk27mOfXNAsZX3\nkbycWaRC3bNIdcr7PS6+Bbwyvy7aO0vZ830t22pAx2OpHnwGcDzW9p0bj2kzIAtp5LnnRO4fUqn/\nyEf6iD+LdKuucdtmH2Bl0mhE36X9ULiVSVoROJBUtztIhff/iRHUb6wi10l/F0vm++goUCc9ch/Z\nSqMrEhF/6yPpm4GLlQbOaR4mt+0ANmMSC+mKxgHANySdDHw38qiYPdQ1FHydaU+52IgIST8BejZi\nGlTakfu4VxrSeJumj86U1M8oYz9WarDzCPAepZ4Kip5/3kBqaHxARNwlaUNytbQeea+6vqqcR6oe\nF98kjVdwNfCzXEezUB3pMmlX3c517JuDiK2Sb0k7R8QFuW5ys6dLIiKKjBB5lqTrSOWC90pam6YR\nNHsodb6va1sN4nhkcQ8+GwOHqHgPPrX8Tg3oOwPTaGRD0ih9p0j6E+mfxgTpJF/US2PJxoTXKjcw\nVGpEMyzHkeoUfT2/fyNp6N7XDzHNQZhDOqkcnd+/kVSo7jl6k6Rnkb7jmvn9PcBbIuI3BdL9Q37M\nAJ7QZ57riiUizgbOVho57k2kUTFvIa2/E6Nzg4xahoKvOe2pGAu5G6aI+HUfMYNIexVJT42ImwGU\nhiZepWjCEXGwUj3pv0bEY5IeIg00VST2LlJ/uY33fySd04qosr6qnEcqbedIo7c2d7N3q6SdCobX\ntZ3r2jfrOqb+GbiA9hfAggJDrUfERyR9EfhLRDwq6f9Ioxr3VOF8D/Vtq0rHI+mPQ6MHn4clrQW8\nrUBcnb9TVb8zMI0aGwIo9WH4jPy231Hrrib1FXxpfv88UmOyZ2tIDQ1zOtdHxBa9po2bKvmW9AtS\nd0Tz8vsdgc9GxAuHktkxkU+qbwTeQmqo+X3Sn4+nRcRLW+ZtdIl4JelK4QwWD+d+QkTc2yWd0rF1\npj0VY1uW03c3TINIW9IrSLefb85pbkTqivKcHnGdrtpBynjHwoakiyLixZIeZMm+YhvfebUC+a7a\n7VVf55EBHBdvjojvSWo74mO3u1N1buccO9J9s85jKscdFBFHSnpxRFxUJK2m2B0i4kJJbUeMjIgz\nCi6n8Pm+Ja6u80jZdb1ZRPxWUttezaLDKJJ1/k41LWMgXedNpyvSAM8DZpO+13PyLZyiV0feDhyT\n/9mIdKvu7Uq9gXxuGJnNrpC0XURcAiDp+UA/t2XrcnXzPzmlqjRFhxtfpfHjBxARk3k99yRpHm06\neY+Incc1NsefQrqFdAKwV0Tcnj86QVK79fY70i3y5uHce/azPYDYOtOeirHNXl4ipnLaEXG2pKcB\nm+VJv41i3X7uQMmrdhHx4vzc992ZJmXWV7N+zyNV13Vj2WW+c53bGUa/b9Z5TEG6Enok6c5Bv93W\nvoxU97/dXeEgNfLsqsT5vlkt55GS6ULqU/0dpIa3rQLo9BtZ5+9UQ9VzEDCNrkhLOh7YBLiKVKcJ\n0j+Lnp3ktyxn9RzYOoLUUEi6gXQVvdFVy4bAjcCjVOy4fpiU6o5tTmolC6le1A2k7n4iuvS5Lek0\n4ArSbVlIIz89NyJeUyDd5za9XZHUtdGjEfHRcYxt/ElS6sP7/OjzgFOqf7lPfqxEuqpxUqSRO4cW\nW2faUzG2ZTl9d+M0gG31QhZfRGikW/QiQt+UBl7pKFq67eyxrLLd0JU6jwxqO5dR93Ye9b5Z1zEl\n6URgG+DJpCp5j3/EEH9Xq57vW5Y18vNI2XSrqPN3qmkZ1brem0YF6RuALcruuEqN5/Zi6ZPU4QPJ\nYOd0N+r2eZQYJGAUJG3S7fOI+EOnz/Itr0+xZEPFT0XJvq0lXRoR245jrAY4kI+krYFjSN03FR2S\nuHJsnWlPpdh8K/jLpB/vu0m33m+IgqNflk277EWETlUUGnpUVVgE3E76ww+pgNIUGk8tkO9K62sQ\n55F+1rV6DD9e4qLNSLZzjq1l36waWyXfkiZIg9YsVUWj2++q0gh9HUWqI98ptvL5vsbzSKl0O1UN\na4hiDTtL5blq/KDW9XSq2nEdqYHhnSXjTyf1NXk5xVvmVtZ8QOfbkq8B9o2IV40qD2U0F5QlrURq\nnLRvdBk+VXmUo/xD19ePTtMymq+EzQCeS6oTNbaxVaj9cO6HDTu2zrSnYmz2aWA70pWorZUaoBVq\nqFwx7W0odxHhS6RC2Vmkc566z76Er5G6mbqYVE/xohLpl1pfVc8jFdb1u0m/M3OBRqP2UaUN5bcz\n1BGuwwoAACAASURBVLRv1nlMRWoI23Wk3Q6+SjouziHdYe17O1dU13mkbLo/IK2vqxrZaPqsZ8PO\nOn+nqLCulxAD6ENvHB7APNIIgedQblCC62rK9/KkwvMppHrZxwK71b0+C+R7Fqlu5YnA/aTbq6/p\nEXNF0+uvl0z3FlJjm1tInbCfC7x4XGPzuul7MAVSPb1jSN0vnkFqtLJKwbyWjq0z7akY27Kcy/Lz\n1cCMxuthp53PHeuVyO+zSSORXkUaiOWlULzfW9IP5k6kBnBXAV8ANh7m+srzlDqPDOC4WItUmJ5H\nGtn07cATR7WPld3OdeybdR5TeZ65+bl1fIhr6TF4DukPy5dymt8Eduwjv6XO93VuqwGs6z1JIxxf\nRhocadNR7CN1fufWx3Sq2rFDu+mR++AsEP8t0kn52oFmrHN6uwD7kjptn0fqwP3rETF7FOmXJWln\nUr5fSbqVejLw1YjoWkUlxz7e+8kgqzyMM0k3kX5w2+q0f6rGoeDrSnsqxrYs53zSj8rnSYWuu0nD\n0HbsjWYQaSs1hN0KuJSmu2kR0bbXgQ7LeCHpuH4p8LEo2DNBjn0i6WrQp4GPR8TRPUIacX2vrxxX\n6jwyqO2cl7UB6Tt/kLS+ju8xf63bedT7Zp3HVI5bLyLu7FR1MgpUmZQkYHvSdt6BtJ1/3COm1Pm+\nZRl1nUdKreum+FVId6bfkOP/vdv3rfN3qmkZlb7z48uZLgXpqiRdT+oG5RYW3+aMGF6jhEWkguhb\nI+KWPO3mKFC3sE5N+d4vIubnaYXy3fyjV7YgrdTF4XtI/YRCuo3zzSjQ1WEdscvKH4ZlmQbU1VeF\n9KteRHgSsDepl4KFwCcj9yLUJab5R/NJpNu3c6NYg6iq3V5VPo9UodTN176kK2KXA1+OiOtHkG7f\n27nufbOsQeRb0kzSLfuifXy3xq9JOib2JpUHPhERv+gRU3p/rGtbDSrdvL5fQfrj8U+kPx49u2as\nw6DX9ZSvI62l+zF9/CMK9mea7Tq4XBXyHNIOd76km0m3RvquXF+DbUn5nqfUB2M/+d5M0jWkbbNJ\nfg39/Wn5f6TheY/K7/8lT+t4FaDm2PkFlo2kl0XEeUXmtbEzqK6+SonU5+1GpP5pz5e0MgWOSUn7\nkwoJK5LqOe4dEXcXTPZuUhWnk/JzANtI2ibnqVu9yKrraxDnkb5JOhx4Fal3opOAQ6L7wBoDVXI7\n17pvVjCI7gIfk7RI0urRRy9ckt5C+oO4GnAq8OaIKNr2an7BNNqd7+vaVpXSzXep9yGVDc4HjoyI\nce/Cd6Drepm5Ii1pjXaX/yWtFhEPqEN3TtFHN04V8ta4rboXqa7OaRHxrWGnW0XTba99SaM9XUrK\n9zFdYir3UCLp6oh4dq9p4xRbhK9cT32qqVs1Se8A3gmsGRGbKPU1/D8R8ZIecYtIjecax90SPwbd\nqgxI+m7r/EuGxv4F8l1qfQ3iPFJGXl+3AA83kmp8xAi6Ki27nXNsbV3+VVE135JOB7Ym1Wl/fLS7\n6NLTSd7O15LaxMDSx0Wh0Q175Kvj+b7G80jZ43ERqf75RaR11bq+SnUsMAqDWtfLUkG67Y4r6ccR\n8Wql4TuD/9/enYdJVpT5Hv/+ugEbhGZtkX1TQEB2ZNVREEYFUYEGGlEEx+WOIojjgjhX20GdGXHB\nxrmyyXZZFHFBUEAREEUBGxAYgREBQUUFFOgLSjfwu39EJJWVlZWVlds5mef9PE89VXmyIvOtyjpR\nkXHeeIPpl3HqYYwzSDmKB9f+EUna3O1tnV0YpVWze5Hifms+tqntOzt8vJ/Z3nmS+24C5jpXDZG0\nIfCNdgahRbVth/q4e2YYPHVZxmmaz3ULaTboeo/lDt9m+6VTtGuaKlDTbmrIFM9xWDszPf34fbXq\nRzp8vEJLlXb6Ojd5nIH9bfZSJ3FLOqzZ8VZ/k5JavjGxfWU7zz1FXG3190W9VtN53sl+xzVDcgWk\nq9/10Kd2TEPTEja298mfNxhsOE1jeZZUDeKKusPnMP2dmQYqX978Xv6oOY/O457V4r4PktJKarMF\n65N2sWpHUW3bUY13tCNM3Zf66tRTtheni0TPxTHl39M0cqgvsr1/h7EdBTT9RzqA31erfmTa2h0o\n93oAX6ej17nue4v42+xKt3HbPkupPOu6tu9qs01bA2VJX7d9YLuxND5Ni8ct5LXq9HnbHShLWmD7\nyC5C7Lle/a5n9DSqcmvZ4UiacPI0O1aAQdew7JVu4p7wWknaQdILcyf3YtLiptobj1+2DKSgtqEa\nJO0p6aukDUreAVwKbGT7YNvfGUAI10j6KLCs0o5qFwLf7eHjd3NVbkI/MMDfV1FvTns6gK8z7de5\nBH+bHelV3JJeTyrNeFm+vbWktivSTOHFPXocoLjXaoDPu2sPH6srvf6ZqzSQbkrSrJwfvZqklSWt\nkj/WB9YqNjpgeGcqex33ycDi/PWOwEeALwN/ItWxLWPb6bivh48VButY4DrgJbb3tX2e7SematRD\nHwEeIuV1vhO41PZxPXz8bs7lZm2L/n31W7/67E5e52H9Xfcq7k+Q0mEeBbB9C929MazXzet8X5Nj\nRb1Ww/o30o2e/syVT+0A3gUcTdoicmHd9z0OnDSAuMJEzV6rmXULPw8CTrF9EXBRzh1spai2z1Fa\nYf8B0iXGd+SFQps41yXtxQKWUAzbuxfxvJLeAKxt+8vAqXkx2hxgO0mP2v5GEXE1mHAuD/D3NaxX\n88bp5nUu6m+zWz2Me4ntx2rpMNmzPXrsSXXS3xf1Wg3r30g3ev0zj8yMtKTPSWq1P3rTBQS2T8z5\n0f9ie0PbG+SPrWyXYSC9eOpvKaVnumj7libHZuZ8Jkiv5Y/q7pvqDWFRbeudQapPXsud/D1w/DTa\nh9DoQ6QdvWqWIW1d/0pSzfNe6WZA+tOeRTF9zfqRQej1AH5Qr/Mo+m9Jh5D68RdLWkCaieyFVq9z\n9PcTjcQb22ZGaUb6DuCUPOg5g1QX8LnakZ66jN0fJa1ge5Gkj5EWyh1v+6b+hZzysN1Qvqj+mO2d\n+vn83ZB0MCmv6FOS1gFeYHshgO0dmnz/ZDW/yW1m58+3N7n7fFKO4MOkAurX5sd8ETBVjdCi2tbb\nyPZBkuYB2H5SDdMkIUzTMrYfqLv9k9zP/UVpw5Re+XDjAUnHtGpg+/P583t7GEftubvpR3oVQ309\n52WBpWwvynf3egA/qNd5FB0JHEca1J4HXE7vBrQfbXFfZft7ScvZfrLJXScOPJgBGZmBtO3TgNMk\nbUKqpnCrpJ8Cp9q+qo2H+FfbF0rajVSC7rOkDTd27Ee8kmYBy5Fzsxl7tzabcuRmtyTpJNIGJa8A\nPkWq0fkVYMIAusb2CrntvwEPkiqSiLSj0Bqtni8P1q/M33eF/VzdxhmkzrJ0bRsszv9wDSBpI+q2\n+Q2hAyvX32gYtM6ZqrGk22g9IN0yf76iyd0r5M+bkM752ozp60k15fumm36kF1RXzxnYCFib1PfV\nJj96PYDv6nWuIklzge/mAd1x+aPdtjfT+rzYNn/+fouHqVx/r7QfxmnA8sC6krYC3mX7nwFsn1lg\neH01UnWklbao3Ic0kF6HtGPNbsATtg+eou3NtreR9BngNtvnqY+1fSUdxVhu9u8Zn5t9aknSSial\nXJe7/nekkmxuUkZ5lf3HgM1IFT92JW0Pf3WRcYXhJelc4GrbpzYcfxfwStvzpmhfq4v8nvz5nPz5\nzQC2P9JGDD8G9q7NxkpagbQI7hVt/yAdKqofUY/qOU/j+bp6natI0rdIfezlpKuKl9tuK90wD3oB\n3k3aObL+vHjG9oQrNE0eo3L9vaTrgQOAi+vOi9ttb1FsZP03MgNpSV8gzYZcCZxu+4a6++6yvckU\n7S8hDWj3JKV1/A24YQCd8pG2F/TzOfohnzQ7A7/IA+pVgR+288ZD0nWkyhcXkN6xzwPeY3uXfsZc\ntPw72on0punnth8uOKQwxCS9APg2aaarloK2HfA84I22/9Tm40yYMFCbO21Kuou0gcFT+fbzgFun\n6m97oah+RNL1tnesm3xZCrjJ/duavCevc9VImg28iVQjeGvgO6SUz3brp084B9o9L/L3Vqq/bzwv\n8rGRniCrGZnUDtIWlR9z8xImL2uj/YHAa4ATbD8qaQ3SJhx9ZXtBviSyPnWvh+2z+/3cXfoycBEw\nR9J80u9vfpttDyHlS51I+gf403xsZEl6E/Aj25fm2ytJeqPtbxccWhhStv8M7CJpd6C20PpS2z9q\n0awZSdrV9k/zjV1ofyH62cANeQYQ4I1MsgFLHxTVj1yj8fWc/5ne1u0ep4evc6XYfpz0t3hWHtQe\nAHxJ0iq212njIWZK2sn2zwEk7UiaoZ5SRfv7B3LfYUlLkzZjuqPgmAZi6GekJbV8dzjVYkFJs20/\nrlRLuln7qRYpdkXSOaQ8u1sYq3Rhl3h/+hqlKimvJr3j/mE/F/cMO0m32N664VhsCx4KJ2k70ta4\nK+ZDjwJHtLvQOvfBL883f2z75t5HWR6SZgBvB/Yi9X2XA6d52P+Zjqi8BukA0hWLFwPfsP3+Ntrt\nQCpcUNtg52+k8+LGNtpWrr+XtBrpTW1tTHAF8L5+j6HKYBQG0q0WEtpT1AuUdIntfSTdS5rVqF9Z\na9u9Kt4+2fPfAWw2TJ1wzkW/1XarcoOt2m9MWsi5uu0tJG0J7Gt7ZMsDSbq18dJvP/MqQ5guSSsC\nuK7aUZvtdiNVsDhD0hxgedv39iPGhuctrB/RNLedDoMlaXlSWsc8YBvSYtgLSLnm0/pfm2ezsf3I\nNNpUrr+vv6rV6tgoGvqB9LCTdCHpXduDRccyHZK+C7zb9u87aHsNKW3m5KosSlDajvRRUkoMpAVe\nq9h+W2FBhQBIWh34NLCm7ddK2gzY2fbpbbT9OLA9abOJjSWtCVxou+/bARfVj0jal1TVaRnbG0ja\nGvik7X37+byhfUrlSi8jDZ4vt72kg8eYQyqVt1aebNsMeJnbqD5Rxf6+25zyYTZKOdK13L716SDX\nWNLb6/9x5FnXj9luN++3U6sBv5J0A3XlcYagU14euEPSz0il74C2d+hbzvYNGl9W8+kex1c2RwL/\nCnwt3/4BY9USQijSmaRL2LUSYf9D+judciBNmvXbhrwIzvYfcuWOQSiqH/k4ad3N1ZC2nZa0wQCe\nN7RvHdt/m+qbJF1ke/9J7j4TOJexOuq/Jp0XZ7bx/JXp7yXtDOxCWi9VX19+Nm3mlA+7kRlIT5Zr\nTFoM0449JO1Pyn1blfSPpa3VvV36xACeox+6uXz6cC4xVKuxeQCpHuzIyotgpywnFkIBVrP9dUnH\nAth+WlK7O5Mutm1JtXN5kBuEFNWPNNt2Oi7tlkg7g+isVermC5zK4H4wP+YSSW1tL16x/n4Z0sTa\nUozVl4dUyveAQiIasJEZSJMuL3aca2z7EEkHAbeRZlgPGURuT7uleMrG9pVdNH8PcAqwqaTfA/cC\nh/YksJKR9EXbR+dUmAl/m0Nw5SGMvidyHmhtQLoT7e/a+XVJJwMrKW1UcgRpU4ZBKKofGbftNPA+\nerftdBisVuOFJ3IRgtp5sQNpcDipKvb3eQxzjaQzbf+26HiKMDI50t3mGucO8SzSQPolwK+AY9x8\nq8ue0fjtbpch7Rb4hPM2t2XVEPdSpEs4T00n7jx7NcNjW+uOHEnb2V4o6R+a3T+sb6TC6MhVNxYA\nWwC3k3bLm2v7l22235O6Cha2f9CvWCd5/oH2I5KWI6XB7JUPXQ4cb/vvg3j+0DutcnjzwPmLpJKD\nvyTtODy3VVWaKvf3Oaf8Q6TfV63SCVMVfBgFQz+QrnvntwKp6HpHucaS7iQV879S6ZrdMaRSNx1V\npuhEft43ADu5jV3FyiKXg9oP2Nr2x1p83zGT3Qdg+/O9jq0sJO1Hqv060tvEhuGjtInKM6TtvgXc\nRRqYTvm3Kuk/3LDTW7NjvVRUPyJpKdujvpajUlqVpFPaaGcGaWJNpMm1Z9v5G6hify/pClJO+L+Q\ndoU8DHion31BWYzCQLrpO7+adt8BKteTbji2se3/6Sa+Tgxrvcmp4s4r/Cc1gIWdhZF0BrA78GNS\nZ3NZ/FMOZdDNavtJ2k4o/dVLRfUj9T+rpAW2j+zH84TealWqUNJetq+YpF0350Xl+ntJC21vV3/+\nS7rR9g5Fx9ZvQ58jXRsoTzYzwhQLBiV9yPZ/Om3KMtf2hXV3vw34aK9jbnj++ioXM0i53qW/RJhL\nQNXU4l7cqs0oD5SnYvtwpd2eXkuqbfplST+w/U8FhxYqStILSZerl5W0DWM19GcDy03R9n+RdvTb\nSNKtdXetQJ/zhQvsR+pXF/a9vF/onqTXAyeQ0iYnlCpsNohW2pJ9DdJ58VKmcV7UVLS/r5UYfFDS\n3sAfgKYb3Y2aoZ+Rrul0ZqRhlmHcYwyiBmJ+51rzNHAfcKrTtrCllauk1NTiPtn2H1u0+VKrx/QQ\n7ObYrdy5vgY4HHiF7dUKDilUlKTDSJMF2wM3MjZgeBw4y/Y3W7RdEVgZ+AzjqxMscv93gy2kH2n1\nvyKUk6SFpJnhq+tqjbfcGEXS4aRFs1sDNzP+vDizYbJtquevTH8vaR/gWmAd0pqL2cB82xcXGtgA\nDP2MdN3MyIYdzoxokq+b3e4524f3+zn6wfZbOmi2sOeBDAlJrwUOAl5Jqj97GnBggSGFirN9FnBW\n7apc/X2aoi6y0+6Hj0l6unGlvqRzOuwf2lVUP7Jp/h8jxs/Ei7QLbt/SWULHpl2q0PYZwBmTnBfr\ntvOkVezvbV+Sv3wMeBUMvBxmYYZ+IA2cB3yfzmdGPMnXzW73nKS1Se/eapcKrwWOsv27fj93NySt\nRnrXvj7jN8B552Rt8j/uqnorKVfuXVVagBKGwsHAfzYc+wawXRttxy3Gzgu02mnXsQL7kZcU9Lyh\nc92UKmx2XnwbaOdKRKX6e0lrkdJhbrW9OKfHHE264rVmkbENwtAPpGszI8A8pd0IVyf9XMtLWt72\n/VM8xFaSHifNKiybvybfnjV5s545g/RmYG6+fWg+tucAnrsb3wF+DvyEsQ1wWqpijc0a2/MkrQe8\nHPhhXgCz1CiX/gvlJmlT0kB4xYa1GrOZou9T2rzlo0zsMxeTajv3TVH9SLs1ciX9zPbO/YghTNuR\npFKFTwHnk0oV/lurBpI2Jr1pWrFhLdCU50VNlfp7SUeTfsd3A8+T9F/Af5A2w+vrm+qyGKUc6feS\ndgn8E1Dbfaj0l9sk3WJ766mOlU0nMaraNTbfAbwTWMX2Rnl25Cu29yg4tFBRkt4AvBHYF6jPY1wE\nXGB7ypk7SZ+xfWyfQpzsOUvdjwxr1aWQSHoTqZzr64Dv1d21CDjf9rVtPEZl+ntJvwJ2s/2XnPry\nP8CutiuTyjlKA+m7gR1tP1J0LNMh6UrSDPT5+dA84PCyn3CSPgNcNVnpoEnarNvGFYKRJOkW4GXA\n9e0ueglhECTtbPtn02yzqe07lTZzmcD2Tb2Jrulzl7ofiYWIxZvsakVNO1ctJO1m+ycdPn9l+vsm\nRRp+aXurImMatKFP7ajzAO1va1smR5BypL9AOvGvI63wLbt3Ax+W9CTpcm5twU2rcjfP5ZdJusj2\n/v0PszSeyrljwHO5pKPxLjYMpbrFVIdImtd4/xTVL44hzbh9rsl9JlVK6Jcq9yOhPSd02lDSB2x/\nDti/IeUJANstNwTKqtTfr91QSWeN+ttVqMY1SgPpe4CrJV3K+J0NS71bXs67G8bc4E7K+NQvnd6w\nV4EMiWsk1XJK9yRVmvluwTGFarsjf/7FdBvWFhXbflVPI2pP2fuRvld7Cq11md7zm/z59i4eo0r9\n/QcbblcmpaNmlFI7mu52VfZNQHKZqSOZWP2i9INrSQcDG9r+dK4+snqrvKgq12FV2kb97cBepH+0\nlwOneVROwFBJeYH33kzsv/o2gVH2fkTSFra7GYSFLkn6uu0DJd3G+JnggZQqjP5+Io3wbqAjM5Cu\nkbQ8gO3/V3Qs7ZD0S+B04DbGFkkWvmBmKpJOApYmFZl/iaRVgMvdYjtQSc8AT5ArpABP1u4idW6z\n+xx2oSTNAbD9UNGxhFAjaXvSqvv1GD8YnnKwIel7pJ1YG/uvvk1gFN2PSFrExMv0j5Fm9j9g+55+\nPn+YmqQ1bD+YK2dM0E4Flpz/fywTz4u23rhFfz9eGd/09srIpHZI2gI4h7wlpaSHgbfa/u9CA5va\n32233KmrpHaxva2kmwHyit1lWjWwPXMwoZWHUpLcx4H3krZSrw0EFtj+ZJGxhZCdS7o8O24w3Ka1\nB10ZqQT9yBeB35HKlopUb3gj4Cbgq6RNOEKBbD+YP/9W0gtJC/8M3OgWu+82OJ80kG77vIj+vppm\nFB1AD50CHGN7PdvrAR8ATi04pnacKOnjknaWtG3to+ig2rAkX74ygKRVmf4/4Sp4P2mznR1sr5IX\nY+4I7Crp/cWGFgIAD9m+2Pa9tn9b+2iz7fcl7dXX6MpnX9sn215k+3HbpwD/aPtrpG3TQ0lI+ifg\nBlI5uwOAn0s6os3mD9v+pu1f2/5N7WOKNtHfV9DIpHY0K7kyDGVYchm5t5AWONTXv+7nqveuSXor\n8CZge9IszIHAfNsXFBpYyeQZ+z1tP9xwfA5wRdSbDUWTtAep7OaVjF+o/c022r4J+L+kSZklVCBN\nS9LPSFWWvpEPHUCaxNlpGPYAqBJJd5Gunj6Sb68KXGd7kzba7kUagDeeFxe3aBP9/SRGub76yKR2\nAPdI+ldSegekHQKHIVdtLmnB3uKiA2mHpKVsP237bEkLgVeT/nnOjQU2TS3d2KlCypuTtHQRAYXQ\n4HBgU9Kah+fezANTDqSBzwM7A7dVaCHVm4ETgf8i/Z5+DhyqtHvde4sMLEzwCGkjlZpF+Vg73gxs\nCazA+PNi0oE0Fe7vJc21fWGLYycWENZAjNJA+ghgPmOd/7X5WNndDqwE/LnoQNp0A7mGa84/L3sO\netFavUEaijdPYeTt0M4M3SQeAG6v0CCavJjw9ZPc3dEGHqG3JNVqPd8NXC/pO6RB8BuAW9t8mJ06\nOC+q3N8fC1w42THbZw46oEEZmYG07b8Cw1j4eyXgTkk3Mv7yUVnL30WN1OnZStLjTY4LmDXoYEJo\n4jpJm9n+VQdta/X7v88Q1e/vRr5M/w4mlvwbhombqlghf/4NY3WhAb4zjce4XtImtu+aRpvK9feS\nXkvaTn2tho1ZZgNPFxPVYA39QFpSq8ssZR6Q1jStf11ic+re7U8wyv9AO1GCCgMhTGUn4BZJ95IG\nw9OptXtv/lgmf1TBd0hXPH8IPFNwLKGJxvKLHZbF3Qa4VdLdjD8vJi0GUNH+/g+k0o/7Mn4zlkWk\nxZcjb+gXG0p6iHR58XzgehpmTMtej7mRpN2AebbfU3QszUh6EPg/TDIzXfYNcEII43VTa7fuMWan\nJl405TcPuVhQODway+ICbZfFlbRRs+NtVO6oJElL216Sv14ZWMd2u2k0Q20UBtIzgT1Jq863BC4F\nzh+C+tHPkbQNcAhp4eG9wEW2Tyo2quZGuah6CFWVS27uRsoj/antm9pstz1wBmOX0h8DjnCLHU6H\nnaTjSZUfvld0LKE1SdcBx9m+Kt9+JfBp27u02X5Lxp8XlRgYdkLS1aRZ6aVIM9N/Jp0nIz8rPfR1\npG0/Y/sy24eRLlHeTcrZK/XqaUkb5/rRdwILgPtJb2xeVdZBdNZWjnR+RxpCKDlJ/xs4C1gVWA04\nQ9LH2mz+VeCfba9ve33gPaSB9Sg7CrhE0t8kPS5p0SR5saF4z68NogFsXw08v52Gko4jXeleC1gb\nOE/Ssf0IckSsaPtxUsnAs23vCOxRcEwDMfQz0gCSngfsTZqVXp9Unuartn9fZFytSHqWlGf3dtt3\n52P32N6w2Mhak7SK7b+08X0xcx3CEMi1drey/fd8e1ngljZr7U6oDRvnfigLSd8i7ThZXxZ3O9tv\naqPtXcA2tp/Mt5cDbu6iws1Ik3QbsBfpTflxtm+UdGubay2G2igsNjwb2AL4HmlDkGGpZbwfaWvZ\nqyRdBlzAEFTEaGcQnZX+ZwkhAGmx0Czg7/n284CWkxB1u69eI+lk0sydgYOAq/sTZrEkbWr7zsl2\nnm03HSYMVDdlcR9k/BhpqXwsNPdJ4HJSCsyNkjYEfl1wTAMx9DPSeWb3iXyz/ocZih22JD2fVNty\nHrA7cDbwLdtXFBpYl2JWKoRyk7SA1GeuC+wA/CDf3hO4wfZ+LdpeNdl9DMHOrJ2QdIrtd07ys4/k\nz1xFkr5AOg/WJ50Xl+fbewE32j6guOhCGQ39QHqU5LziucBBtveoHcs1sodKDKRDKDdJh7W63/ZZ\ng4olhF7qpiyupLdP0fb0TuMaZZI2JlX0Wt32Fnmh5r62jy84tL6LgXTJDeuAtFnuZAhhtOSFihPY\n/uSgYxkUSXOBy2wvyosytwX+zfbNBYcWslErizsMJF0DfBA4ufa/X9LttrcoNrL+G/oc6Qooba5x\nLj24OuN397o/f1mJ1bohDLu8EcuEGZU2Fz4/Uff1LGAf4I4ehVZW/2r7wlzz/9XAZ4GvADsWG1ao\n80LGyuIeQgdlcSX9mubnxca9CnLELGf7BmnckCV2NgylUMpLBpKOJO3K+Cfg2XzYpFre01mUGEIo\n1vZ1X88ipZetMsn3jmP7c/W3JZ1AyikdZbXdDPcGTrF9aa4tHUrC9jPAZcBluarXPFJZ3PnTKC+7\nW93XtfNixd5GOlIezpvYGEDSAVRkcWakdpRcWVM78rapO9p+pOhYQgi9JWmh7e06aLcyaUHWi/oQ\nVilIuoRU1WRPUlrH30iLM7cqNLAwTj/K4kr6he3tp/7O6slVOk4BdgH+Stpc7s3T2SF1WMWMdPmV\nNbXjAdIuZiGEIdZQzm0GaYa6rf8NuXZsbTZmJjCHVAZrlB0IvAY4wfajktYg5YaGkuhFWdy8TSyy\nOQAAEEFJREFUWK6mdl48rzcRjhZJM4Dtbb86VyKbYXtR0XENSsxIl0CrXON2N0AZNEmnA5uQcs+e\nqh23/fnCggohTFtDObengftIg8S72mi7XkPbP9ke6bzIfPn6d7afyltOb0naye3RYiMLNb0oiyvp\n2rqbtfPis7Z/1as4R0mVZ+tjIF2wyXKNy74bkKSPNztue/6gYwkhDFbe5W2J7SX59ibA64D7bH+r\n0OD6TNItpNnJ9Ukznt8BNrf9uiLjCqFIkv4deBj4GnWLkMs4EdhrMZAuWOQahxCKIOn1wK21HMZc\nym5/4LfAUbbvbdH2x8Dbbf9a0ouAG4Bzgc1IOdIf6fsPUJDauhVJHwL+ZntBlPscHZJeB9xed1X4\no4ydF++vQs5vJ3L1n0Zus/rPUIsc6eINZa6xpDnAh4DNSSuaAYjdvUIYGp8CdgKQtA9wKGlh1jak\ncm7/2KLtyrZr2/8eRiotdqSkZYCFwMgOpIElkuYBbwVen48tXWA8obc+Q1owh6S9SVuKv5l0XpxM\nyo8PDWxvUHQMRZlRdACBe0hleY6VdEzto+ig2nAucCewATCflD92Y5EBhRCmxbafzF/vB5xue6Ht\n00iLBlu2rft6d9L24thezFiK2qg6HNgZ+JTteyVtAJxTcEyhd2y7lpqwH3Ca7ettf4W0lik0IWk5\nSR+TdEq+/eL8Bn3kxUC6ePeT/gktA6xQ91F2q+atUpfYvsb2EaR/qCGE4SBJy+cV93sAV9bdN2uS\nNjW3SjpB0vuBFwFX5AdcqT+hlkdebPZh4KZ8+17b/1FsVKGHZuRBoUjnxY/q7ouqHZM7A1hMns0n\nlYisRH31SO0o2BAvzluSPz+YL3/9gTY3cQghlMIXgVuAx4E7bP8CQNI2TL2RwjuAo0gL7vaqm9ne\nDDihL9GWRM4tP4E0+bGBpK2BT9ret9jIQo8sAG4mpVz+2vYNAJK2Av5YZGAlt5Htg3LaE7afVMM2\nh6MqFhsWbFhzjfMlm2uBdUgdz2xSvc6LCw0shNA2SWsBLwB+afvZfGwNYOm6xVabT2dr5YbHv8j2\n/j0LuAQkLSRdfbu6tsBQ0u22tyg2stArktYlpXHclHdJrJ0rS9u+L9/e1PadxUVZLpKuI83g/zQv\nxt2ItHbiZQWH1ncxI128c0nlYvYB3k1auPNQoRG1wfYl+cvHgFcVGUsIoTN5l7ffNxxrnI0+h7SD\nXydGccX+EtuPNUy2jXpeeKXkN5H3Nxxr3BHxPDo/L0bRJ0jbsq8j6VxgV9J6gpEXOdLFG8pcY0lr\nS/qWpIck/VnSRZLWLjquEELPdXN5dhQvef63pEOAmXlB1QLguqKDCgNXibSFdtm+grQ4823A+aSd\nDq9q2WhExEC6eONyjXN+4jDkGp8BXAysAawJfDcfCyGMllEcDHfjSFIq3lOkWcnHgKMLjSgUIc6L\nOpKutP2I7UttX2L7YUlXTt1y+EVqR/GOl7Qi8AHGco3fX2xIbZlju37gfKak+GcSQqg3crN2eWHl\ncfkjhEqTNAtYDlhN0sqMnfOzgbUKC2yAYiBdsCHONX5E0qGkSziQNnKI3RlDGD2Lu2j74Z5FURKS\nfgDMtf1ovr0ycIHtVhvYhNHzTNEBlMS7SFdk1iRtxlQbSD8OnFRUUIMUVTsKlvOKFwC7kS4VXUva\nnvd3hQY2BUnrkeLemRT3dcCRth8oNLAQwrTkS7J7THVskra7khYZrUeamBEjvi1ws+3AY4vw0STp\nYFJZt09JWgd4ge2FRcdVRpKOtL2g6DiKEDPSxTuDlGc3N98+NB/bs7CI2mD7t8C4uqk5teOLxUQU\nQpiOHl2SPZ2UiraQ6szQPStp3brygOsR+bIjR9JJpK3fXwF8CngC+AqwQ5FxlZXtBZJ2IdWWX6ru\n+NmFBTUgMZAu3ijlGh9DDKRDGBa9uCT7mO3v9yG2MjsO+Imka0i/s5cD7yw2pNAHu+R6yDcD2P6L\npGWKDqqsJJ0DbETa5Kn2ptpADKRD341SrvHILSwKYVTZPhE4sctLsldJ+izwTVIVi9pj39SLGMvI\n9mWStgV2yoeOtv1wkTGFvlgiaQb5aoOkVYl64a1sD2zmCuYLx0C6eEeQco2/wFiu8duKDKgLlTuB\nQhh2XV6S3TF/3r7+IRmCWvhd2oV0yb/mksm+MQytLwMXAXMkzQcOBOYXG1Kp3Q68EGjc0GnkxWLD\nEpJ0tO1SpkhIWkTzAbOAZW3Hm7MQhshkl2Rtv6+4qMpL0r+T8mTPzYfmATfa/mhxUYV+kLQ58GrS\n/7cf2r694JBKS9JVwNbADYy/OrXvpI1GRAykS0jS/bbXLTqOEMLok3QHXVySlbQ3aYOSWbVjtj/Z\no/BKR9KtwNa2n823ZwI3296y2MhCr+TX9Fbbmxcdy7CQ9A/Njtu+ZtCxDFrMHpZT5BqHEAal40uy\nkr5CqvzxKuA04ADSjNSoWwn4S/56xSIDCb1n+xlJ90hay/bvi45nGFRhwDyZGEiXU1wmCCEMymrA\nryR1ckl2F9tbSrrV9nxJnwNGvYrHZ4Cb86VskXKlP1JsSKEPlgfukPQzUuk7AGzvV1xI5TNFuqdt\nzx5wSAMXA+mCTJVrPOBwQgjV9Yku2v4tf35S0pqkikNrdB1RSUkS8BNSxY5aPeEP2/5jcVGFPjm+\n6ACGge0Vio6haDGQLkj88YUQyqDLS7KXSFoJ+CxwE2ly4NSeBFZCti3pe7ZfClxcdDyhf2xfWXQM\nYTjEYsMQQqiwhqtjy5B2c3tiupdkJT0PmGX7sR6HWCqSzgJOsn1j0bGE/mk4L5YCZgJPVSFVIUxP\nzEiHEEKF1V8dy6kLb2Bss5GWJC0N/C/GaipfLelk20t6Hmh57AgcKuk+Uu5sLRc0qnaMkIbzYgaw\nH6m8WwjjxIx0CCGEcSTdbHubNr7vNNIM9ln50FuAZ2z/Uz/jK5Kk9Zodt/3bQccSBqvd8yJUS8xI\nhxBChUmqr0Iwg7RL4d/bbL6D7a3qbv9I0i97FlyJSJoFvBt4EXAbcLrtp4uNKvSLpPqqNbXzYnFB\n4YQSi4F0CCFU2+vrvn4auI+U3tGOZyRtZPs3AJI2ZGx3xFFzFrAEuBZ4LbAZcFShEYV+mlv39XTP\ni1AhkdoRQgihI5L2AM4A7iHlCq8HHG77qkID6wNJt+VqHUhaCrjB9rYFhxVCKFjMSIcQQoVJWhtY\nAOyaD10LHGX7d1O1tX2lpBcDm+RDdzG6C7KeW0Bp++m0LjOMKkmrAUcA61M3VrL9zqJiCuUUM9Ih\nhFBhkn4AnAeckw8dCrzZ9p4dPt79ttftVXxlIekZxna4q22c9SQV2sGtSiT9FPg5sJC6dCXbXyss\nqFBKMZAOIYQKk3SL7a2nOjaNx3vA9jq9iS6EYnRzDoRqmVF0ACGEEAr1iKRDJc3MH4eStvruVMzO\nhFHwfUl7FR1EKL+YkQ4hhArLdZEXADuTBsHXAe+zfX+LNt+l+YBZwO62n9+PWEMYFEl/BVYkpe8s\nZiyFZ5VCAwulEwPpEEII0yLpH1rdb/uaQcUSQj9ImtnsuO1RLe8YOhQD6RBCqDBJGwBHMrE6wb6T\ntZnGY19ke/9uHyeEIkg6GNjQ9qdzdZvVbS8sOq5QLjGQDiGECss7EZ5O2q3v2drxXswqx5bKYVhJ\nOglYGniF7ZdIWgW43PYOBYcWSibqSIcQQrX93faX+vTYMVMThtUutreVdDOA7b9IWqbooEL5xEA6\nhBCq7URJHweuAJ6qHbR9U3EhhVC4JZJmkN8MSlqVuis2IdTEQDqEEKrtpcBbgN0ZGyg43+5WbP8X\nhtWXgYuAOZLmAwcC84sNKZRR5EiHEEKFSbob2Mz24j489l62r+j144bQL5KWsv10/npz4NWkN4Q/\ntH17ocGFUoqBdAghVJikbwPvtP3nabS5jcnrSNv2lr2KL4RBknST7W2LjiMMj0jtCCGEalsJuFPS\njYzPkW5V/m6fvkcVQjEiHSlMS8xIhxBChU22uUpsqhKqSNLvgM9Pdr/tSe8L1RQz0iGEUGGNA2ZJ\nuwHzgEkH0pIWMT61Q/l2LbVjdh9CDWEQZgLLEzPToU0xkA4hhIqTtA1wCDAXuJdUraCVK4EXAt8E\nLrB9f38jDGFgHrT9yaKDCMMjBtIhhFBBkjYmzTzPAx4GvkZK93vVVG1tv1HSisB+wKmSZuX2F9j+\nSx/DDqHf2pqJlrSy7b/2O5hQfpEjHUIIFSTpWeBa4O22787H7rG94TQfZwZwMPAl4NORQxqGmaRV\n2nkzGNU9Qk3MSIcQQjXtRxoAXyXpMuACppEXKmkX0mz2y4GfAG+yfW0/Ag1hUKZxRSVyqAMQM9Ih\nhFBpkp4PvIE0KN4dOBv4VquNVCTdBzxKGnz/CHi6/v7YXjyMupiRDjUxkA4hhACkvE/SgsODbO9R\nO9aYCyrpappvyAKpakcvthcPobRiIB1qYiAdQghhUjFgCGEiSTfb3qboOELxZhQdQAghhFKbkAsq\n6UN1X89tuO/TgwgqhH6TNFPSmpLWrX3U3b1HYYGFUomBdAghhFaaXbY8uO7rYxvue00fYwlhICQd\nCfwJ+AFwaf64pHZ/lHkMNVG1I4QQwnRpkq+b3Q5hGB0FbGL7kaIDCeUWM9IhhBBaaTYw9iRfN7sd\nwjB6AHis6CBC+cViwxBCqDhJM4HVqbtKWdv2u9kGFZKeAZ4gDbKXBZ6s3QXMsr30IOIOoV8knQ5s\nQkrpeKp2PDYcCo0itSOEECos54J+nJQP+mw+bGBLaJ4LanvmwAIMoRj3549l8kcITcWMdAghVJik\nu4EdIxc0hBCmL2akQwih2iIXNIQGkuYAHwI2B2bVjsdmQ6FRDKRDCKHa7gGulhS5oCGMORf4GrAP\n8G7gMOChQiMKpRQD6RBCqLbIBQ1holVtny7pKNvXANdIurHooEL5xEA6hBAqzPb8omMIoYSW5M8P\nStob+AOwSoHxhJKKgXQIIVRY5IKG0NTxklYEPgAsAGYD7y82pFBGUbUjhBAqTNIVpFzQf6EuF9T2\nhwsNLIQQhkDsbBhCCNW2qu3TgSW2r7F9BBCz0aHSJK0t6VuSHpL0Z0kXSVq76LhC+cRAOoQQqm1c\nLqikbYhc0BDOAC4G1gDWBL6bj4UwTqR2hBBChUnaB7gWWIexXND5ti8uNLAQCiTpFttbT3UshFhs\nGEIIFWb7kvzlY8CriowlhBJ5RNKhwPn59jwgdv8ME0RqRwghVFjkgobQ1BHAgcAfgQeBA4C3FRlQ\nKKcYSIcQQrVFLmgIDWz/1va+tufYfoHtNwL7Fx1XKJ/IkQ4hhAqLXNAQ2iPpftvrFh1HKJeYkQ4h\nhGp7RNKhkmbmj0OJXNAQmlHRAYTyiYF0CCFUW+SChtCeuIQfJojUjhBCCONIOtr2F4uOI4RBk7SI\n5gNmAcvajmpnYZwYSIcQQhgnckFDCKE9kdoRQgihUeSChhBCG2IgHUIIoVFcqgwhhDZErk8IIVTQ\nVLmgAw4nhBCGUuRIhxBCCCGE0IFI7QghhBBCCKEDMZAOIYQQQgihAzGQDiGEEEIIoQMxkA4hhBBC\nCKED/x/4hvvGI/OHdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10128f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb1, train, test, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=1, missing=None, n_estimators=140, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'max_depth': [3, 5, 7, 9], 'min_child_weight': [1, 3, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test1 = {\n",
    "    'max_depth':range(3,10,2),\n",
    "    'min_child_weight':range(1,6,2)\n",
    "}\n",
    "gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5,\n",
    "                                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), \n",
    "                       param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch1.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83690, std: 0.00821, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       "  mean: 0.83730, std: 0.00858, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       "  mean: 0.83713, std: 0.00847, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       "  mean: 0.84051, std: 0.00748, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: 0.84112, std: 0.00595, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: 0.84123, std: 0.00619, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.83772, std: 0.00518, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       "  mean: 0.83672, std: 0.00579, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       "  mean: 0.83658, std: 0.00355, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.82690, std: 0.00622, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       "  mean: 0.82909, std: 0.00560, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       "  mean: 0.83211, std: 0.00707, params: {'max_depth': 9, 'min_child_weight': 5}],\n",
       " {'max_depth': 5, 'min_child_weight': 5},\n",
       " 0.84123292820257589)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=2, missing=None, n_estimators=140, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'max_depth': [4, 5, 6], 'min_child_weight': [4, 5, 6]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test2 = {\n",
    "    'max_depth':[4,5,6],\n",
    "    'min_child_weight':[4,5,6]\n",
    "}\n",
    "gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=5,\n",
    "                                        min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch2.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84031, std: 0.00658, params: {'max_depth': 4, 'min_child_weight': 4},\n",
       "  mean: 0.84061, std: 0.00700, params: {'max_depth': 4, 'min_child_weight': 5},\n",
       "  mean: 0.84125, std: 0.00723, params: {'max_depth': 4, 'min_child_weight': 6},\n",
       "  mean: 0.83988, std: 0.00612, params: {'max_depth': 5, 'min_child_weight': 4},\n",
       "  mean: 0.84123, std: 0.00619, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.83995, std: 0.00591, params: {'max_depth': 5, 'min_child_weight': 6},\n",
       "  mean: 0.83905, std: 0.00635, params: {'max_depth': 6, 'min_child_weight': 4},\n",
       "  mean: 0.83904, std: 0.00656, params: {'max_depth': 6, 'min_child_weight': 5},\n",
       "  mean: 0.83844, std: 0.00682, params: {'max_depth': 6, 'min_child_weight': 6}],\n",
       " {'max_depth': 4, 'min_child_weight': 6},\n",
       " 0.84124915179964577)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
       "       min_child_weight=2, missing=None, n_estimators=140, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'min_child_weight': [6, 8, 10, 12]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test2b = {\n",
    "    'min_child_weight':[6,8,10,12]\n",
    "}\n",
    "gsearch2b = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=4,\n",
    "                                        min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test2b, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch2b.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84125, std: 0.00723, params: {'min_child_weight': 6},\n",
       "  mean: 0.84028, std: 0.00710, params: {'min_child_weight': 8},\n",
       "  mean: 0.83920, std: 0.00674, params: {'min_child_weight': 10},\n",
       "  mean: 0.83996, std: 0.00729, params: {'min_child_weight': 12}],\n",
       " {'min_child_weight': 6},\n",
       " 0.84124915179964577)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2b.grid_scores_, gsearch2b.best_params_, gsearch2b.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
       "       min_child_weight=6, missing=None, n_estimators=140, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'gamma': [0.0, 0.1, 0.2, 0.3, 0.4]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test3 = {\n",
    "    'gamma':[i/10.0 for i in range(0,5)]\n",
    "}\n",
    "gsearch3 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=4,\n",
    "                                        min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch3.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84125, std: 0.00723, params: {'gamma': 0.0},\n",
       "  mean: 0.83996, std: 0.00695, params: {'gamma': 0.1},\n",
       "  mean: 0.84045, std: 0.00639, params: {'gamma': 0.2},\n",
       "  mean: 0.84032, std: 0.00673, params: {'gamma': 0.3},\n",
       "  mean: 0.84061, std: 0.00692, params: {'gamma': 0.4}],\n",
       " {'gamma': 0.0},\n",
       " 0.84124915179964577)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until cv error hasn't decreased in 50 rounds.\n",
      "Stopping. Best iteration:\n",
      "[177] cv-mean:0.8451166\tcv-std:0.0123406045006\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.883836\n",
      "AUC Score (Test): 0.848967\n"
     ]
    },
    {
     "data": {
      "image/png": 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mg9KqID2/cPvtwC8AIuIpYEHZDUiaIOkmYC6pMH49sE5EzMvx5gJr56evB9xfWP3BvMzM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\n6t1K+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122519790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "xgb2 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=4,\n",
    "        min_child_weight=6,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb2, train, test, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tune subsample and colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
       "       min_child_weight=6, missing=None, n_estimators=177, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'subsample': [0.6, 0.7, 0.8, 0.9], 'colsample_bytree': [0.6, 0.7, 0.8, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test4 = {\n",
    "    'subsample':[i/10.0 for i in range(6,10)],\n",
    "    'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "gsearch4 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4,\n",
    "                                        min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch4.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83688, std: 0.00849, params: {'subsample': 0.6, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83834, std: 0.00772, params: {'subsample': 0.7, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83946, std: 0.00813, params: {'subsample': 0.8, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83845, std: 0.00831, params: {'subsample': 0.9, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83816, std: 0.00651, params: {'subsample': 0.6, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83797, std: 0.00668, params: {'subsample': 0.7, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83956, std: 0.00824, params: {'subsample': 0.8, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83892, std: 0.00626, params: {'subsample': 0.9, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83914, std: 0.00794, params: {'subsample': 0.6, 'colsample_bytree': 0.8},\n",
       "  mean: 0.83974, std: 0.00687, params: {'subsample': 0.7, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84102, std: 0.00715, params: {'subsample': 0.8, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84029, std: 0.00645, params: {'subsample': 0.9, 'colsample_bytree': 0.8},\n",
       "  mean: 0.83881, std: 0.00723, params: {'subsample': 0.6, 'colsample_bytree': 0.9},\n",
       "  mean: 0.83975, std: 0.00706, params: {'subsample': 0.7, 'colsample_bytree': 0.9},\n",
       "  mean: 0.83975, std: 0.00648, params: {'subsample': 0.8, 'colsample_bytree': 0.9},\n",
       "  mean: 0.83954, std: 0.00698, params: {'subsample': 0.9, 'colsample_bytree': 0.9}],\n",
       " {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       " 0.8410246925643593)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tune subsample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
       "       min_child_weight=6, missing=None, n_estimators=177, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'subsample': [0.75, 0.8, 0.85], 'colsample_bytree': [0.75, 0.8, 0.85]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test5 = {\n",
    "    'subsample':[i/100.0 for i in range(75,90,5)],\n",
    "    'colsample_bytree':[i/100.0 for i in range(75,90,5)]\n",
    "}\n",
    "gsearch5 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4,\n",
    "                                        min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test5, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch5.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83881, std: 0.00795, params: {'subsample': 0.75, 'colsample_bytree': 0.75},\n",
       "  mean: 0.84037, std: 0.00638, params: {'subsample': 0.8, 'colsample_bytree': 0.75},\n",
       "  mean: 0.84013, std: 0.00685, params: {'subsample': 0.85, 'colsample_bytree': 0.75},\n",
       "  mean: 0.83967, std: 0.00694, params: {'subsample': 0.75, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84102, std: 0.00715, params: {'subsample': 0.8, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84087, std: 0.00693, params: {'subsample': 0.85, 'colsample_bytree': 0.8},\n",
       "  mean: 0.83836, std: 0.00738, params: {'subsample': 0.75, 'colsample_bytree': 0.85},\n",
       "  mean: 0.84067, std: 0.00698, params: {'subsample': 0.8, 'colsample_bytree': 0.85},\n",
       "  mean: 0.83978, std: 0.00689, params: {'subsample': 0.85, 'colsample_bytree': 0.85}],\n",
       " {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       " 0.8410246925643593)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch5.grid_scores_, gsearch5.best_params_, gsearch5.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Got the same value as assument and no change requried."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0.1, learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
       "       min_child_weight=6, missing=None, n_estimators=177, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'reg_alpha': [1e-05, 0.01, 0.1, 1, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test6 = {\n",
    "    'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "}\n",
    "gsearch6 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4,\n",
    "                                        min_child_weight=6, gamma=0.1, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test6, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch6.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83999, std: 0.00643, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.84084, std: 0.00639, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.83985, std: 0.00831, params: {'reg_alpha': 0.1},\n",
       "  mean: 0.83989, std: 0.00707, params: {'reg_alpha': 1},\n",
       "  mean: 0.81343, std: 0.01541, params: {'reg_alpha': 100}],\n",
       " {'reg_alpha': 0.01},\n",
       " 0.84084269674772316)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch6.grid_scores_, gsearch6.best_params_, gsearch6.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0.1, learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
       "       min_child_weight=6, missing=None, n_estimators=177, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'reg_alpha': [0, 0.001, 0.005, 0.01, 0.05]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test7 = {\n",
    "    'reg_alpha':[0, 0.001, 0.005, 0.01, 0.05]\n",
    "}\n",
    "gsearch7 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4,\n",
    "                                        min_child_weight=6, gamma=0.1, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test7, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch7.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83999, std: 0.00643, params: {'reg_alpha': 0},\n",
       "  mean: 0.83978, std: 0.00663, params: {'reg_alpha': 0.001},\n",
       "  mean: 0.84118, std: 0.00651, params: {'reg_alpha': 0.005},\n",
       "  mean: 0.84084, std: 0.00639, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.84008, std: 0.00690, params: {'reg_alpha': 0.05}],\n",
       " {'reg_alpha': 0.005},\n",
       " 0.84118352535245489)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch7.grid_scores_, gsearch7.best_params_, gsearch7.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until cv error hasn't decreased in 50 rounds.\n",
      "Stopping. Best iteration:\n",
      "[188] cv-mean:0.844475\tcv-std:0.0129019770268\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.887149\n",
      "AUC Score (Test): 0.848972\n"
     ]
    },
    {
     "data": {
      "image/png": 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i4t35+e7A5hHxwbb3jFfSZmZmZjZtdWtsuNioE6ngdmCttudr5GnzdFsYMzMz\nM7NRGceqHRcDT5e0tqQlgF2BUyY5JzMzMzOzBYzdFemIeFzSvwNnkgr6/xMR105yWmZmZmZmCxi7\nOtJmZmZmZlPBOFbtMDMzMzMbey5Im5mZmZnV4IK02SSStLyk5Sc7j5IkrTzZOTQlaaUOf4tPdl5T\nnaQVp9v+XkI+DrxQ0opjkMsLJjuHElrf28nOw4ZnXL4306qOtKRnAv8NrBoRz5W0EbBDRHy+4vwH\nkXo576i9L+seMVYFvgg8LSK2lbQh8OKI+J8K864cEXe3Pd+dNNLj1cDBMcDGknQVvZdlo6qx2mKe\nGxFbDTjPZX3y6HvQlrQCsC/wemCVHO8u4GTgyxFxf8VcisTpEf+0iNi2wvvWAL4MbAP8DRCwDKmB\n7Sci4s995l8T+CppoKLTgK/mUUCR9NOIeH3FfJcnrY81gNMi4qi2174bEe+rEGNb4LukLio/APwY\nWApYEtgjIs6pEGMD4JvAE8AHgU+TttEfcoxKjY3z9n0N8wdwuh04o852lXQzsCZwH2n7PBm4E5gL\nvCsiLqkR86qIeN6A83y7w+S/Ar+LiJMHzWHQXErsa5KeRtrfdwSWY353pocAX2jFGyD3D3eY/Ffg\nkoi4vM+8xfaRLvGrHgN+DHwoIu6WtA1wMGl/fwbwkYg4vmEer46Isyq8b+LxV6Tj4fak8sGlA3ym\nSOer9nV7UdXzVj4ePTUi/jRh+kYRcWXFGGsBXwFeCdxPWp7lgXOBfSLi5ipxesSvtF47zLcusAlw\nTURc1ySHtpiXR8TGA7y/U/ml9b25umKMN3aJcVVE3NVn3j0j4pD8eA3gMOCFwDXAOyLiDxU+f6jf\nm/wZGwy6jaZbQfp84KPA9yNikzzt6oh4bsX59+j1ekQcViHGacChwCcj4vmSFgMuq3jSurRVsJT0\nKeDlwFHA64DbIuI/KixGK9ba+eH78/8j8v+35mXZp8/8Ew9cAp4JXJ/nr1QQl7R+fvhvwMwJeTwe\nER+vEOMM0oHwsIi4M0+bBewBvDIitq6YS+M4Pa7WCPhZRKxWIcYFpMLncW2FksWBXYD3RcRL+sx/\nFnAi8BtgL9LBaPuIuEfSZa19v0IeJwI35Dh7Ao8Cb4mIf7Tvi31iXA7sRipo/gx4bUT8RtKzgSMr\nxvgFqbC2HKnA9XHgWNJ+/6GIeGWFGG8nDft1JvMLamsArwY+ExGH94sxId7BwAkRcUZ+vjWwE+m7\nfWBEvKgB0enbAAAgAElEQVTLfJ1ONJD2j+9FxFMHzOMHwAZA6wSxE3AT8BTgxoj4UI95G+dSYl+T\ndC7w2YiYk3N6OfAp0o+4VVqDb1Ul6ShgU+DUPOl1wJXAOsDxEfGVLvMV2UcKHQPm/ZCR9GvS9+7m\nfDfnnIh4fpVcesT/c0SsVeF9T5C27T/aJm+Rp0XVCyf5+/Fd0vGkfd0+nXRMO7PP/DsD3yJd2Fic\nVLC6OL9W6ViU33thjnNCRDyep80E3kw6lmxRJU6P+FXX67wfmZJ2zDnNAV4CfCkiflTx83bo9hLp\n4toqVeLkWMcAm5GO0wDbkb4365KO1V+vEOPnwIuB8/Kk2cAlOcZnI+KILrNOLN8cB5wN/JD0A/vf\nKx7nh/q9yXErbeMFRMS0+QMuzv8va5t2+VTJYcI8lwLL5seLk37x1cnnsg7TLq0w3ymkq4sbAGuT\nTlK35sdr18hjoc+skkd+3/V1XhtGHOBxUmH8vA5/j1SMcUOd17rtT8DuwO+B9auu0y5xPglcQCqk\nVd02l7Y9vrVX/B4x2vf7P9bdR4And5i+IvCHquukbb6Fvm/Alf2Wi/Rj5EekAvfEvwdr5PEbYGbb\n88WAC0k/Sq/pM2/jXErsa8AVE55f0vb4uhrr5BfAcm3PlwPOB5butU5K7SOFjgG/B5bPj38FzGh/\nrWKMU7r8nQo8VDHGTnndbds27aYa2+RaYJ0O09cFrq2ynwGr5cebA9cBb8jPFzqH9YjT6NhacL22\nH9N+DaybH6888fvQJ86jpPPwER3+Bjqe5O38pLbnT8rTlul3LGmb5wzSHf/W81XztJWAq/vM236u\nmHhMqLSNS3xv8nu/3eXvIOCBQdZrRIxfP9IN3Z2vgAaApDcBd1SdWVLPgV8iotuvw3YPSXpKWw5b\nkG59VLG0pE1IddcXj4iH8uc+KunxijEmkqSXRsQF+clLqFA3PiJ2kPQG4AfA1yLiFEmPRsQtNfOY\nKWmLiPhNzuNFpMJAFbdI+hjpSvLcPP+qwDtIhfuqSsS5FnhPRNww8QVJVWNcnm/ZH9b2uWvmPK6o\nMP/ikpaKiL8DRMSPJd1JOqAtWzEHgCUlzYiIJ3KcL0i6nVxQqRjjfknvId0+vU/SfwDHAa8iVVup\non0/+MaE15aoGEN0rkL0RH5tUHdI+jhwTH6+CzA3X916osd8V5K+LwvdKpX0qhp5rEjaFq1jyLLA\nSpH62/9H99mK5VJiX/uLUjW184A3AjfnHES9djqrsOAV1EdJJ/dH+qyTUvtIiWPAZ4DzJH2H9OP1\n+Hz+eQVwesUYLyf9sJn4PWtVsegrIk7Md+o+J2lP4D/pURWvh8WA2zpMv510IaifmRFxR87pIkmv\nAH6mVLVokHwukfRdFj627gFcVjFG4/XKgjkvERE3AUSqktDr+DHRVaQr2L+f+MIA+1rLqsAjbc//\nQfrePFzhWNKyZuvcmd2Vp90rqV8VrTXyeU/AypIWj/nVuqq2PynxvQF4J2lf77Tcuw0QBxjDAVka\nej+p4LdBLhDcRPpCVPVi0pfvaOC31DsBf5j063X9fAv/qcCbKs57B/MLEndLWi0i7sgF88dq5ALp\nduwhSnUDIdUb27PKjBFxkqQzSQfZvaheqOnkX4FDJS2Vnz9SNQ9SIWYf4HxJrVtZc0nreecBcigR\nZ3+6n/w/UDHG7sC7gQOYX5/wNtIVj49WmP+HwItIVxMAiIizJb2ZVD+wqlOBrUi32FpxfpQLSgdV\njLEH6Tb9E8DWpIPQGcAtwLsqxviOpOUi4m8R8d3WRElPb8+tjy8Al+b9tXWCWYt02/5zFWO0ewup\nGsBP8/ML8rSZ9N5XPgQ80OW1N9TI4yukH15zSMejfwG+KGlZ+q+bErmU2Nf2BL5G+u5dDvx7nr4S\nqXrHoI4EfiupVUd8e+CovE6u6TFfqX1kfxoeAyLiOEmXkr4jzySdi7cAjo5cnaiC3wAPR8T5E1+Q\ndH3FGETE34D/yBdxDqP6j+h2hwAX5+oD7QXYXYG+7YOAByWtH7l+dD7vzSZ9/54zQB5vJ53zPsPC\nx9YqeUCZ9fp8SQ+QvrNLtp3Ll6D6BSRI5YluFyTePEAcSNXlLpTUOqbtABybvzdVl2uOpJ+xYFWz\nOTlGv3YG7ee235H2s/uUqldWGr260PcG0gjaV0fErye+IGn/AeKkefJl7mklb9QZEfHggPPNJB1U\ndwM2An5O2kAL/RrsE2cx4FmkL9H1MUBjmnyVZs1oa3CW81oyIh4eJI8JcVcAiIiqV8dbuawREbdK\nej6p0eT36uaQYz4l53FPkzhdYu8RFeqxjyJOoRgfiy71PSvOv29EfKlJDjlOiWVpnEu/GEott7dh\n4YZk9zX53GEYZH1IWo35V8Iujoj/m6xcxiWGpM1I9U0BLoiI31Wcb2T7yLh8bwb8PJFu/z8wYXrf\nPJTaRezIguv2lIjo9eOmNe/zSdUm/jhh+uLAzhFx5ACL0deo12vb5z4ZeHZEXFg4bqVzhdId8pfm\npxe07hAP8DkiFZ7nxQBOjIIFyRGdK1YC/t6kTLVAvOlUkM476dtJ9XnnXW2PCr1tdIi1JKlA/VVS\nQ5T/qjhf7VatbTEGbt3fI1btXkRK5iLpqcDngdUj4nU5j82jYqOLip9RuVHKsOOMQwyvj44xLoyI\nF1d43zOBj7DwsWSgXmt6xB+kAdXqpLYJ7Xn8okQeg+YyLjHyxYVVWXCd9OztZtTGZb2WUPBYcmJE\n7FQip4Z5jOxYMgpVlycXhJ/Kgt+boj/Mmxqn703V/XW6Ve34X9JtmavoXY+xq1yAfi2pEL0OqQL6\nSQOE2IsurVol9WzV2uZSSZtFbrXc0I/IvYjk538g3eKpepurVC4/It2SbfXScUPO40cN47arUxVn\nWHHGIYbXx8KW6v8WIN26/B6pakPd9gm9VFoWSQeQqiT9nvnHtCDVYx9pLuMSQ9IHSNVu5pK2Tavu\n88BderbFLHbxoj3sZMUYwvKUOpasN/AHj++2qXosWfCDJ2l5JL0P+CxwDwt+bzas/CHpQuEBpHYK\nasWIiJJ9w4/L8Qgq7q/TrSC9VER06mO0EkmHA88lFcg/06mRTgWLkW7dtDdmO5xUz/AXzO/+rZcX\nAW+VdAvwEPN31jonipVzvaJ9SUEe02ANF0vlskpEHCXpozmPRzVYo4sqSt1eKRFnHGJ4fdSP8VhE\n/HeBz2uax+uBZ0VE1cZAw8xlXGLsTVonA1UP63K3ENIxbdYgsSoa6joZ8fIM9VjibTO8XNp8mFQ2\n+UuDz/kKqfvLSn371zQux6PKcaZbQfoISe8i9ZM478QTEfdWnH93UmFxb+CD6S4IMNivriatWlu2\nqfi+Kpr0IlIyl4dyvaRWHpvRvSFUXb4CWz6HUnHGJUZVp+YrOCdR71jST9VluZHUon2YBelx2TZV\nY9zKYMewlmNJd8U6nRxrXV3sY9jrZJTLM+zv3lTcNr2M4/LcBjQ9fs0dciEaxud4VNl0K0j/k1Sn\n+ZPM34GDipfnI6LEkOlNWrW28rgFQKlniaZfuia9iJTM5SOkltPrKQ2cs/ogeVR0wRjFKRHjJw3n\nbzzKU1ZiWUrkUiJG1QPsHvl/e0vzyseSCqouy8OkXjvOYcEC/cDtPgrkMi4xbiQdU3/OgutkYteJ\nE5XumrCfYX9vRrk8pY4l3b5/o942wz6WjHp5qpwr/gicm8sn7d+bTqOndvM7SceSelNpj9H0XNVu\nXI5HULW62TRrbHgjqQHb3X3f3Hn+rSLi3Px43ch9P+bnb6yys+TK/G8EXpYn3Ufqq/H93edaKMYO\nwNeBp5GuaK9N6tR+kG6A2uM16UWkWC5KXf88O+dxTUT8c8D5GzWcbIvTuFFqiVyUunf7DjAr0iiY\nG5FGBqzdYlnS/4uIzw44T5H1WiKXJjFyA7SzI+IVPd7z3JpVtipTGrp2DdJIWze3TZ83RO4Asfbo\nNL1AbxCDrNdiy9Mkj7Z59us0PSI+02e+lwO3dGqUKGnTqNjzR9s8jY4jTddrqeUZ5vbt8FlbR4dR\nDktvmy6fPbJjyRD2tcbnCkkdu3iMiE8PEOPQziGiale23eKO9FwxQMyO++tC75tmBekzgddHzS5N\ntOAQlgu0+hywRfkmpD5n30zqy/rEqNjrR57/CnL/vhGxiVLn9LtHxF4DxNgqIs7tVler6i/IprlI\n2jIizleXoU4jolL/kTlW7eHXJ8T5NR0apQ5SOCmRi1LfwJ8AvpPXrUh9W9b6wZRjDjy8aan1WiKX\npjHylds3xgDdPE6Yv9H3RtIXST+iLyX1b/ytiDgovzYWPTBA9fU67OUpsY+UpordbzU5joxyP+m1\nPKXykHQVPeqT1mhT0+1zaneNNupjScXPqLqvzaHwuWKcjPpcUXp/nW5VOx4i3QY9j3q3QdXlcafn\nC76YusvaLf/dTaojpV6/aHt4NCLukTRDaeS58yR9a8AYW5KGsd2+w2tB9WoDTXN5NWkwh06dxwcV\nO2LPmjacbGnUKLVgLstGxK+V6+JHRKhCPXqljv47vkQaJnlQtZelRC6Fl+dvwFWSziIdD4CBjgFN\nvzfbA5vkdbg/aaCQ9SLiPxig3p6k4yJi5w4H/MqNfQut18bLU2r7SvpWRHxI0ql0OAlGtZFnq3gz\nUKWw1uQ4UmQ/qajX8pTK43X5f+vOa6tR/VsHTbaPnttmzI4lVVTd12qdKwAkfT0i/lPSSXT+3nRr\nGNke42MR8RVJB3WJ0XedjNm5ouj+Ot0K0j9l/khkdUSXx52eT3Qd8EvgdZE7lVcaLrmO+yUtl+Md\nKeku2r7IVUTEfvn/O2vmUCSXiPhU/v+2hnlA84aTLU0bpZbK5R5J67bFeD1wZ4X57gc2iwUbtZJj\nDDpsLDRblhK5lFyen9CgbnmB781iEfFYjnG/pO2BH0g6nsFGBt07/39dz3f1VmK9llieUtu3dbL7\n2gDz1FG1ANnkOFJqP6mi1/IUySPmt6V5dURs0vbSPkoj0e1TI+9O+m2bsTmWVFR1X6t7roB0UQ+g\n8l3xDloNDJtUsRmbc0Xp/XVaFaQj4jClerjPzJMGqg9Magh3Cmnnbj0mP1+3z7xvJA2Hep6k04Fj\nGPDKgtL48UeTRod6hDTE71uBFUj9Pw4Sq+eVkujTMKdULpJ6/lKNwRo6NGo42aZRo9SCufw7qT/v\nDZS6F7yDtA/1cziprvpCBxPgqAFzgGbLUiKXYsuTjwFLA2tFROVhkieStDepusuDwMHAC4B9KtSX\n+5Nydaacz+PAXpI+T2p4XElE3JEf3g08EhFP5LteGwCnVQxTYr2WWJ4i2zciLsn/5w3drDRS4ZoR\ncWXVOFU+quL7mhxHiuwnFfVantJ5SNJLI+KC/OQldB9OvY5+22bsjiX9Pqbi++qeK4iIi/L/c1rT\nlEY6Xj0qjDqZ5z01/59XbUnSDGC5mDAKZg9jda7Iiuyv062O9GzgMOBmUiF2TWCPqDgKmKQte73e\nfgDvEWNZUuFzN1Ld4sOBkyqcgFsn712B1YDjSMOTX1Yh9U6xOjbIaYn+DXOK5KIuDRza8qjc0CHH\nq91wsi1Go0apJXPJcVYgfRcr9eqS5xF5+PY6n9khXtNh7RvlUmp58hW1rwFLRMS6kjYGPjvobX9J\nV0SqL74N8G/Ap4Ajok+d0XzihVRd5tYJr60eEbcPmMclwMuBFUm9QFwM/DMiKt2CbLpeSy1Pyf1V\nqb7oDqQLQZeQGkFfUKC6Viv+ZROuUnV7X+3jSOn9pM9ndV2eIeyvLwQOIV1wgXQFcc+IuHTAtLvF\n77ttxu1Y0uczKu1rbe8f+FzRNu85wBuAmaQ68fcC50bER3vOuGCMo0jHw8dJx6LlgQMj4qsV5x+b\nc0WOVWZ/jYhp80c6qD6r7fkzgUuG8DknVnzfisC7Sa2hB4m/NmkEwMtIVUb+H/DMSVqn45TL+4En\nT1i/76sR50xgmcnOJc/zDeAi4Lek3lFWHGD+q8ZlvZbIpVCMS/JB8bK2aVfXiHNl/n8g8Ib8+LIB\n5i+1bS7N/z8AfCw/vnyKbptS6+Sy/P9fSQNnzdteheJ/ouL7ShxHiqyTpstTOo/8HVxhMpal1PKU\nOpYUWp5G54oco/W92Qv4XH480Pemdewh3Z3+OqmP+0FjjM2xpC1eo/215C2XcbB4tN2CiYg/kDZ0\naVX7pb4vIn4QEa8cJHhE3BIRB0T6pbob6VdkrU7QJa0n6VRJf5F0l6STJVWuwlAqF0nrSDpJ0p35\n70RJ6wwY5l3R9ks8Iu4D3jVoLsxvlPp9Sd9u/U1CLseQqg68lTQY0APMr89WxaVKA9s0VWJZSuRS\nIsajsXAr+zojaF6i1AvQdsAZkp40YJxS20aSXkzaR36ep80cMMa4bJtS62QxSasBO5PqJzcm6f+1\nHkfEFyvOVuI4UmqdLKDG8hTJQ9Kqkv4HOCYi/ippQ0mVe5vqErPOthmnY8kCai5P03MFpO/NU0kN\nHE8dcN6WxSUtThpx9ZRIdy4HrdYwNseSUvvrtKojTeos/IfAj/Pzt9Kscnw3Q60Pk2+zb0uqWvFK\nYA6wf81wR5H6n3xDfr4rqe7zi0acy9HAD4Bd8vO35GkvHiDGTEmK/BNSqa/POg1zmjZKLZXL6pEb\nt2WfkTRIH8elhm8vsSwlcikR4/eS3kJapmcAHwR+PcD8LXsBGwM3RsTDSqNyDtIAsdS2+RCwL6l6\n2O/zj+DzBowxLtum1Dr5LHAG8KuIuDivkxsGjDHRvzJgOxTKHEdKrZOJBl2eUnn8iNyVZn7+B1KB\nr0mf9HW2zTgdSyaqszxNzxUAXyD1oPWriLgof29u6jPPRN8nVZ29AviFpLUZfITicTqW/IgC++t0\nqyO9JOk2dWswlF8C342IosPrakj9wUp6Nemq73akWzjHACdHxEA9dkyIeeXEnatV/3OUudTNY8L7\nvwasRfoyA7wHuDUi/nOAGDOBw6NiHdMh53Ig8MuIOCE/fyPw8khdT1WZf+1O0yO3SB4gjxLL0jiX\nQjGWIR0UtyYdXM8g3cb8e9UYOc5LSbcxH5K0O6mx4YFVcym1bSbEHLRxT7FcxiVGE+rTdVZEVL6w\nVPA4UnudFF6eUseSiyNis/a6v5Iuj4iN+8xXbFlyvEk9lgxheRqdK4ZJ0ryeXyq+f2yOJXX314Xi\nTLOC9LLA3yO1PG4d7JaMmgO09PicgRoIDBD3XNIV5BPz7fUSMQ8gja54DOlK+i6k+lZfhe5dNZXO\nRdKXST0QtOexMvDlnEffwkEuSLwbaA2xehbww9b2HiCXXwFbxYAjK5bORdJ9pLpZrYZ9izO/27mI\niJUqxllg+PboMJpWn/mLrNcSuZSK0ZSkK4HnAxuRrlr8ENg5Ino2SO4Qp+m2adS4p2Qu4xJD0leA\nz5N6EzqdtI3+IyJ+3Ge+P9Oj66yIWHPAPBofR9piDbxOSi9P3TwmzD+H1NvHWRHxAqWuNA/o970Z\nxrLkeSflWDKEfa3xuULSl0h9Vj9Mqia2Mel7U7m3Cy3Ym9EPgU2o1ptRp1jjcCyZQ439dSFRsML2\nZP+RRplaru35csCvh/A5W0/2sg6Q6009/m4cYR639vj7c4X5ZwJHFsrlcFKh5NOkrt8+DHx4gPmL\n5JLjdP2rMP8OpFvaD+Xt+QTw+xo5lFiWErnUjkGq83dKt78ay9Nq5Pf/gL3ap41qfeQ4JRr3TOq2\nGeI6eQPpFuwKwBUV5vs8qZeNTq8dUCOPRseRAvt8seUpuG1eQOpd5q/5/x+AjSZh20zqsWQIy9Po\nXJFjtL43rycVhleq8r2ZEOOK/H8bUh/bz2GA42KpfW2y99eJf9OtjvRSEfG31pOI+Fu+PTMQdR4+\n8q+k+tafjxq/viZLRPTr/3okouYVhbb5H5e0tqQlovkVoD/lvxnAkyYxl6NIBYGzIn+rB/Q5YAsm\nDN8+SICCy9I4l4YxWoN0vBGYxfx2ErvRuc/Rfh5UGulxd+Bf8lX7QRoul1gfsGDjnv+KiEclDbqv\nTPa2KRkD5rfteS1wfKRGQn1niohPKVkzJnSdFREfr5FHo+NIVnudFF6eItsmIi5V6kZ2oK40h7Bt\nJvVYMoTlaXqugPnfm+1I35t7axxLWl+07Ujdgf5eVb58CxqbY0nd/XWi6VaQfkjSCyL3AajUR+Aj\nNeKcRrqV2rrlsSuwDGkkoR/RefjgsSRpKeB9pHrjQao3/r0YsM5ogTyWJNW9bc/j4Bis/vqNwAVK\nA+W0D9nac3CZiSL3oa00YiPtP75GnMuhpEZt35F0LPCjyKNiVlRiKHkosywlcqkdI3If70rD4W7a\n9tKpkuo0ON6F1CB2r4i4U9Ja5OpQFZXaNiUa90zqtikcA+Bnkq4jHdvfq9QTQaXjWUSEpP8Fnlfj\ncyfGKnEcabROCi5PozwkbRUR5yrV3W33TElERN8RAktuG8bgWFJ4eZqeKwBOU2qg+Djwfkkr0zYi\nZ0Wt3ozWBfbV4L0ZwRgcS0rsr+2mW0H6Q8Dxkv6P9OtiFvN7iRjEq2LBxoRXKTcwVGp4NJUcTqrP\ndFB+/hbSULtvHnEeh5G+tAe35fEyKo7OlJW4AoSk55LWwUr5+d3A2yPi96PMJSJOB05XGp3traRR\nMW8iraOjo38DjsZDyWcl1muJXErEWFbSehFxI4DSsLrLDhiDiLiT1G9r6/mfSd+lqopsm0gjf7Z3\nqXZLvvoyiHHZNqXWyT5K9aT/mu+oPEQaBKuqSyVtFhEXD/rZ7QodR0qskxLL0zSPfwHOpfNFpqD6\nUNtFtg3jcywpsjwFzhVExEclfRW4NyIek/R30lX3QUzszegpDNabEYzHsaTU/gpMs8aGAEq3QZ+V\nn9Yd+e4KUt+6F+Xnm5EaXz1fQ2poOCySromIDftNW1TyyJ/7a+CTEXFefj4b+GJEvGQSclmR9KPi\n7aTGmEeRfmA8IyJe1WWe1vDtl5Guys1g/vDtR0bEPSNIvVguJZdH0mtI3SzeSPoxvTbwnog4o+L8\nv4qIl0l6kAWrd7W6V1p+FMsiafeI+LGkjqP1VblbMC7bpuA66XYVCaDyVaR8NfvpQKOus5ocRwrv\n87WXp+C22TsiDpT0soj4VdXcO8RptG3G6VhSYnkmxBr4XJHn2zIizpfUcUTGiDilwmdvEBHXSerY\nW1lUGAlwzI4lRfbXlul2RRpgM2Ad0rK9IF+mH+RKEqR+Hg/Jv3hEupX6r0q9gnypZLIjcKmkLSLi\nNwCSXsRw+tbu54r2X+ZK1W4GGnJc0nl06MM7IrYaMJdlWye/PP+cvG1Hmouk40m3/Y4EdoqI2/JL\nR0rqtW7+QKpm0D58+2FVP7dDHk2WpUQuxZYnIk5X6vN1gzzpuhig+lBEvCz/r3tlvtSytPbH2nde\nCuUyLjEAtqTMVaRtanx2J02OIyW/w02Wp1Qe7ySNAvptUgOuuppum7E5lmRF9rUG5wqAV5P6j+50\nFzpIjSj7+U/SIF1f7xJjqp0rSu2vwDS7Ii3pCGB94HJSPSBIv/4+WDPeCjnAxNGNpgxJ15Ku0Le6\nhVkLuB54jDId/1fN42rg2czvAH5d0giJj+Y8+u7MufDdshSp25rHIuJjA+ZyEnAp6bYspEYKL4yI\nN3Sfq1wurR82Sn11nx01v4RK9WV3zX9Lk65QHBNpRM9B4jReryVyKbg8L2H+j2mAyj+mlQZe6Sq6\ndBfZIU6RZSlhXLbNOK2TnE/TrrNKHEeKrZMmy9M0D0lHA5sCTyNVE5v3EvWu9jfdNpN+LJkQp9by\nlDpXjJNxOJYU31+nwXaZJxcaN2y6syk1jNuJhb9Ag45GNOnUpePylhjdYAjr98njT71e7xH3oojY\nfMB5VgQ+w4INHz8TDfvLrpqLhjCgj6RNgENIXfcMOoR0p3gDr9eSudSN0fTHtKQngNtIPzRhfiv1\nVpz1qubSFnPgZVGfoaYbXByYtG3TNEa3ai4tVaq75Dg7kK6sPQ24i3TL/tqIeE6V+dviFD2ONNjn\niyxPgTxmkQYtWagKQdXzTOllyTEn5ViSYzRanhLnCkk9843UDqNfjJ51qWPAxnltcSfzXNF4f22Z\nblU7riY1MLyjYZyTSd3dXcLgrVrHSvsOkW87vgHYLSJeO+I85hWUJS1Nahy0W0RUbiQ04WrhDOCF\npLpRVedfLCIeyye6WgWRUrmUoELDt5dYlhK5FFqeTWn2Y/rbwCtIfYoeTRpOd+BYBZbl30jHs+OA\nVuPpWsZl2xSI8TVSoeY00nG57jpp1HVW4eNIiX2+cVdgJfKI1EC38ki1XRTp1mxMjiVQrsvHJr5F\n+t6cQboDXOd7c0KOcXl+vsAFBgZonDcmx5JS+ysw/QrSKwPXSLqItgJwRHSsZN/DGhHxmqKZTRJJ\nS5D6W30Lqb7WicD3JiGP1o7/lvz/VFJXgoO4hPSlFemK4U2kVsRVXUSuDyXpoIj4wICfXyqX9ZS6\nmuuo3/6qzsO3vzvqDyVfe1lK5FJ4eRr9mI6ID0kSMBt4G3CQUndP/x0RN/WcmaLLshqpTuMupG1y\nLHBCRNxfNcC4bJuC62STHOe1pH32aOCcGgWdpt1vNT6OFN7nay9PqTwkHRcRO2vhMRgGvVXetFuz\nsTmWZE33tUbnimxzUoFzO9KgdUdHxJwBcoDUu8eupFFET84xBup+b5yOJQX31zTTNKvasWWn6ZH7\nhRwgzg+AgyLiqiKJTQJJW5N2uK2B80gn4oMiYp0R57EV83f8X+Y8vhURPaucDCmXeT2uDKN6xQB5\n3EBq0NpRv/1VQxhKvq4SuZRcHqWGkxuTDrJNfkwj6cmkk8fngE9ExMF9ZhnKtpG0Rs7jw8DHI+KI\nPrMUy2VcYnSI+RLSceVVpHVSpcFUa96zSQPcfBl4CumW+2ZRsdeeEseRwvt87eUplYek1SLijm5V\nCQeo2tF024zVsaTA8jQ6V0yIJeDlpGPJlqTvzc+qzp9jLEu6m7wLaXk+WTWHcTqWlNpf58WbTgXp\nUk+LsnwAACAASURBVCRdQ+qy5ibm30Ic+FfKZMp1PX8JvKN1JU3SjXXqeBbKY4+IuLlJHkpdG76X\n1AckpNs534+KXRy2n/SaFqSb5DKZhfhOmq7XcdL0x/SEE8VTSbcsj4sBGzuVotTd1G6klveXAF+P\niGsmI5dxoTQAy86kK/aPAp+O3CtRn/lKdZ1V7DjSRKnlKZjPTFIVhkH7OR+7Zck51T6WDGNfaypX\n4Xsz6bsj4FMR8esBY8wEXkMqjD+PVBiv3B3gOGmyv040Lap2aOE+X+e9RIW+XzvYtnlWk+4FpJ39\nbEk3km6BNG6EVkPrttJ5Sn1qNsnjv0nDNH83P39bntb1F/sEG0i6krRfrJ8fQ70fSk1yubnKB0h6\ndUScNUBOdTVdr2MjUn+pa5P6Vj1b0jIMtr/dBdxA2k9vIB1XNpW0aY5fq1HNoCR9llSF4dqcy75R\nYdCF6UzSnqRCwFKkOps7R8RdA4Qo1XVWyeNIE0W7wWwq0uA4T0haIQbv6WqslgUaH0tKLc/NVd7U\n61wh6e2kCwPLk6p27h4RA1VXyXeWdyWdz88GDoyIyehGt5iG++sCFqkr0pJW7HU7QNLyEfGAunSB\nFRW7vho3bbdBdyINNXxSRPxgxDm0bivtRqpvdVHO45ABYlwREc/vN63H/MV6MGmaS8XPGMnVrlEs\ny6hIehfwbmCliFhfqR/Y70XEKyvO/yM6/yiHVEjas0ymffN4gnRH7OHWZ7deYordHSslr5OrSYNb\nwITtVPWWu5p3nTUWPSG1NF2ewrmcTKrLfhZtI81F9V5zxmlZGh1LcoyRLE+vc0X+3lxFGlgGFv7e\n9B3dMMe4EvhVnn9ijEaNbidL0/11XpxFrCDds2Ai6WcR8TqloTdbja9aYtTVIkqTNINUp3DXVoFA\n0nNisCFtS+SxGKnu9q4R8fY8bYOIuK7PfJcCb47cA4ik9UgNsEp3JXdhRLx4snPRiEbRHNV6HQVJ\nl5Oumvy2rR7rVRHxvMKfs8cwr5iNW2FtHHS71d4ySH3RtphFu42cELvvcaS0YS5Pxc/fo9P0Ot+V\nMViWoseSIe9rXc8VknoW/CPinArxO27XthiTevegrlL767So2jGAnt2+RMTr8v91R5POaEXEE8CZ\n+a/lCAqM7DNgHo8B/5v/Wo6qkMdHSVVEWr+s1yGNUFTaUv3fMpJcRvUrd1TrdRT+ERH/TDdA5v1o\nG8Z63BsY2smjakF5Mgprk2WAeu4nRsROPV4v0m1kBVWOI42NcHn6iojDlLo3XSsirh90/nFaFgoc\nS0a4PF3zqlJQBlDuyaJLjErHOjXvDWukmu6vLTMK5jQVVPoSSFpox+s0bZqo3T9tYV3zkLSZpFn5\ngPAMUgOw1o+CK4aQS9f9ZBJyGZrptCxtzpf0CWBppa6Sjid1tVjauHxvRlJYm2I63jmU9GpJh5AG\n3HkX8HNg/YjYNSJOHkIeQ/0hPAnLUyWn7Ul9DZ+en2+sHt23tc03dstCg2PJmC5PP88oEOOlBWKM\nTN39daJFrSDdk6Slcv3olSWtKGml/LcOsPrkZjc041K3p1ce3wf+mR+/CNgH+A4wFxhpXe8R53Jz\n4XgTjdN6LWUf4C+kOoHvBn4eEZ8cwudMhe/NoqrbOtkX+DXw7IjYISKOivp9r4+DcVye/UnVIe4H\niIjL6fLDZoJxXJYmx5JRL8/NBWIsiseS/am3vy7AVTsW9B7gQ6ThPC9pe/8DwH8NMS/rbWZbQ89d\ngB9ExInAibkeW2m99pNiueRW4P9Juq30rtyY5VmR+/as0gikoVGv16GRtCNpIKXvAAfnhkJPBV4o\n6f6IOKH0RxaOZ0MWEVuN+COHuo9MwvJU8WhE/LVVHSJ7ot9M47QsJY4lpZdnDM4V01Wt/XWiaXVF\nWtLXJfUaw75fpfsDc/3oj0TEehGxbv57fkRM14L0P/u/ZSQe7/HazFzXDNI2PLfttWH8GHzbiHI5\nlNRPeauO6+3A5weM0cSo1+swfQxovyW3BGmo89mkPrJLu2AIMetwgX5h47JOeh1HpqvfS3oL6djy\nDEkHka7MTiWjPpZUMYpzRYnvzbh896oqsr9OtZNlP9cCP8iFg0NJfTfO6x8wqndfd6ekJ0XEg5I+\nRWoE9/mIuLR8ysMl6ZyJ3fW0T4uILUaYy66kemJfkLQmsEpEXJLz2KzHrEeT6qvdTerc/pc53tOB\nyv0/qnt/4+Qcls//rx52Ltn6EbGLpN3y5z6sCT+Nh6zksky2JSLi1rbnv8rf93uVBlmpRNKHe70e\nEd/I//+9XpqD04J92f7/9u483q6qvP/455uEUQizyDxZQKDMyOgACFVBlCFIEAegVVuLgP6qRW0x\nFLVWHDDYylSmH4K1iDIogwiIohBDMFCBiowqKqBAfqCQwPf3x1on99wx99y7s9c+5zzv1+u+cs8+\n957zJNnDOms/63lWAKbZXpCf7sfB2pJ8dGm+eEXnkV51HPBx0qDva8C11Ds5UIVKziUVq+Na8bHx\n/qCkFW0/N8JTp1cYTx0q2V97svydpC1IVQdmkmaOzrZ9Ywe/P9/2tpL2Iv2jfg74Z9u7LpWAlwJJ\nywMrktqDv56BT4rTgWtsb1lzPGeQmn681varci76tUsYQLf//m6k4vbXtXLNJG0OrNTpBxxJ/wI8\nRqpYIlLHqXVs/3OdsUi6lTQT/CPbO0rajPTh79Wd/H0mo8p/15Ik3W/7laM890vbm43zdU7O324B\n7MLAzNRbgNttHzXpYDugCmrZ9gpJdzH2ALbW2tqTPY/0EkkzgCtt/7l0LJNV1bmk4pgmfK2QNI+x\nj5txV+1S6klxDun6sKGk7YD32f678b5GE1S9v/bcQFqp7eOBpIH0BqSuQnsBz9o+YpyvMc/2DpI+\nA9xl+2uqqaZvVSQdz0C+968ZnO99dt2pKso1vNv/HVWo6cdI71silrwS/BPAVqRKGXuSWrrfVGcc\nvUDSxcBNts8esv19wOttz+zw9X4AHNCa+ZW0Mmmx0WvH/s1qqaa62N1AA7W1P5D/vCj/+Q4A2/9Y\nczyNOI80gaTLSeeva0l3uq61PVa6XmNVfS6pKKYJXyvyoBvg/aTOjO3HzYu2x30HR9JtwGHAFW3n\no7ttbzPe12iCqvfXnhpIS/oiaeboBuBc27e3PXef7S3G+TpXkQaf+5HSOv5Emo3quhOkpONsz25A\nHLeR8rt+mgfUa5D63Nf+4SR/uv8KqfWySXcuPmB7jwKxrAHsRvqg8xPbT9QdQy+Q9HLgW6RbdK2Z\n9J2A5YC32f5dh693H6lxwvP58XLA/PGeQ6oi6Tbbu7Z9uJ8G3FH37GuTjDSpoZq6gA55z8acR5pA\n0nTgYFLN5O2Bb5NmTTtulFNS1eeSCuOa1LVipGOk0+Nm6Pkob+vKD49V7q+9liM9H/iERy4z08nt\n8sOBNwKn2X5K0jqkphVdx/bsfDtmY9r+v21fWHMoXwEuA9aSNIv0bzyr5hhajiTlcp1OugD+KG+r\nlaSDge/bvjo/XlXS22x/q+5Yup3t3wN7SNoHaC04vtr298f4tbFcCNyeZy4A3sZSbMAyhps1uJbt\n37F06mJ3E0na0/aP8oM9KLNwvhHnkaaw/QzpGLkgD/oOA74saXXbG5SNbvyWwrlk0iq6VkyVtJvt\nn+TX2JU0Q92JR/PxZknLkBpT3dPhazRClftrT8xISxrzE9V4cz0lTbf9TM7fHel1xrtYsTEkXQRs\nRio63rp1YXfYS76iWLYmtSgXaTa6HxfjLCbpTtvbD9nWVSlEvSyfV16TH/7A9rwCMUwBjgX2Jx03\n1wLnuBdO3BMkaSdSq+VV8qangGO6Kae/l0lajTQomUlq8vHftk8sG1V3q+JaIWkXUhGGVhOnP5GO\nmzkdvMaapA+Orev4dcAHu3Fs1FLF/torA+mxFhLa46zpKOkq2wdKepA0w9C+Kta2Oy7UXZqke4Ct\nSl54c976fNtjlSasTV5M9x/A2ra3kbQtcJDtWleXKy9qHbKtL/Nfm0hpsfFf2D5P0lqkBTYPFohj\n0i1se5GkVQDcVpmp5vdvxHmkCSStRLpNPhPYgbRI91JSrnH3DzIKq/JakWdfsf3kBH538Z2gsbY1\nXdX7a08MpMPoJH2D9InxscJxXAm83/avS8aRY7mZlKpzZskFE0otZJ8ipb1AWkS1uu331BlHGE6p\nesfOpKYHm0taF/iG7Vpb4Eo6iFQ1aFnbm0jaHjjF9kF1xtEkktYGPg2sa/tNkrYCdrd9bs1xNOI8\n0gRKJTSvIQ1GrrW9sHBIPaWKa0WeDDgVWC9PGG4FvNr2+R28xqTzrJug6v2113KkW/lyGzOJfGBJ\nx7aflPOM6idsl8rpnYw1gZ9Lup20eAKAAhfilYB7JP0YWJzD7jIdmVa0fbsGl+FcVCCO44B/Ar6e\nH1/PQEWCUNbBpJmKOwBs/0apckfdTiat77gpx3GnpE0KxNEk55NuUbfaNf8v6RiqdSBNc84jTbCB\n7T8t6YckXWb70DoC6jFVXCvOBy5moM76L/Lrnb+kX5S0O7AHaY1Te6396XSeZ90Ele6vPTWQHi0f\nmLRwqBP7SjqUlJu4Bumk3VUrj9t8snQAWZNudz6hVBLIAJIOI9WDrVVeFFtrya4wbi/YtqTWPlKq\nEcNILWz7/Tbimrb/S9JJALYXSSpRaq0R55EmGM+gJOu69MgmqOha8XKnUr7/kF9zoaTxtsNeljQZ\nNg1on1B4hpRf3FWq3l97aiBNuhU76Xxg20dKejtwF2n29MhuywFqaUrpIds3lI6hzQeAs4AtJf0a\neBCordGGpC/ZPiGnuwzbV/v5tn2D/JekM4FVlZqiHENqRFC3QS1sgQ/SfS2Xq/ZszvNsDWB3o0wn\nzqLnkS7V7x8CO1LxteLZXEihddzsQhoIL1EeR9ws6XzbD3fwnt1uXPtrT+VIV5UPnC9YF5AG0q8C\nfg58yCO3xGw0DW5nuyypu+Czzm1sC8UxjXQ76Pm64xgS08uAKR5ot1zX++5ke66k1430fFM+/PS7\nXG5ucbUM29cXiGFFUgrD/nnTtcCp7oEOchOVq6nMBrYB7gbWAmbY/lmheIqcR7pRN+bTllTltSIP\nnL9EKun3M2A90nEz7mpEOc/6I/k1WtU/GG9Bh24z3v21J2ak2z6trUw1+cBXkgrr36B0T/VDwBwG\nakp2DduLb8Pkv8tbSUXdS8YxBTiEVAS9NkNyu9q3A2D7C3XEYXtu/nYNUn3S58f6+VA/SZ916vh1\n/Qjb6nj/abYX5Q/vH2cgHzjA/wCvI7VxF3AfNdaRbsp5pEtpyT8SWiq+VswD9iZNDoo0QTje1I6W\ni0l51QeSOiW+G3h8EjE13bj2156YkR7t01pLpzN8yvWkh2zb3Pb/TiS+pum0/mSvxJErMYyq7sWk\nks4D9gF+QDo5XWO7XxcrNcooq9OHlaCq4/0lzbZ9XB3v2w1KVw5o2nmkacYq1yhpf9vXFQirq1Vx\nrajiuJE01/ZO7edCSXNs79JJLE1Sxf7aEzPSrYHySDNGkj7LOBcKSvqI7X9zasoyw/Y32p5+D/Cx\nqmKui6T2qhhTSHnktd8WzmW8hsbxQp0xNO0CZ/tope5QbyLVs/yKpOtt/3Xh0PqWpL8ldQ/cTNL8\ntqdWpt7c5PaZkFpL7jWVpFeQbkevIGkHBv6NpgMr1hVH084jTSLpLcBppDTCYeUaYxA9MZO5Vii1\nPF+HdNz8JZM7blpl4h6TdADwG2DEBnbdoKr9tSdmpFsmO4s0ZBZo0Gt1a25X/iTbsgh4CDjbqQ1q\nnXFcNEIcZ9r+bY0xfHms512g2yNAPkG+ETgaeK3tNUvEERY3+VgN+AyDV8kvcI3du8Y6F/UrSe8m\nTWjsTEq1aw0IngEusP3NmuJo5HmkCSTNJc2c3uSB2trRZKoiE7lWSDqatFh6e1J6R/txc/6QCcMl\nvdaBwC3ABqR1CtOBWbav6OTv0RRV7a89MSPdNou06SRnkTTK9yM97gq2jy4dA4Dtd5aOAZi75B+p\nj6Q3AW8HXk+qE3wOcHjBkPqeU5e8pyUtGro6XdJFNe7HW+ZzmRg8O64UZj0pJk1i+wLggtadw/bn\nVG9t7UadRxomyjUuBZO5Vtg+DzhvlONmw07isH1V/vZpUr51ydKgVahkf+2JgTTwNeC7TH4WyaN8\nP9LjriBpfdInx9bt4VuA423/quY41iR9Kt6Ywc1y3ltXDPlC3CTvIuW7vS8WHDbOoIXFkqYBO9X4\n/q+q8b26zRHAvw3Z9t/U9P/TwPNIk0S5xqWjimvFSMfNt4Bx3e2StB4pRWS+7RdyysgJpLtE604w\nptIq2V97YiDdmkUCZip1IVyb9HdbSdJKth8Z50ttJ+kZ0qzPCvl78uPlR/+1RjuP9EFjRn58VN62\nX81xfBv4CfBDBprl1EoNq99se6akjYDXAN/Lix6mRRmtcpSafHyM4cf/C6SawbUYb61WST+2vfvS\njqcJJG1J+oCzypC1H9Op8fzctPNIwxxHqjDzPHAJqVzjvxSNqAdM5lohaXPSB/NVhqxVGvdxI+kE\n0v/r/cBykv4d+Cyp2V2dEwxVq2R/7bUc6b8ndfL7HQNlXfryNmiLpDttb7+kbSXiqJsaVr9ZqdHH\ne4HVbW+WPxF/1fa+dcYRhpP0GdsnlY5jSZpSgacOkt4KvA04CGjPyVwAXGq7lpnPpp1HQu+bzLVC\n0sGkcrNvBr7T9tQC4BLbt4zjNX4O7GX7Dzkd5H+BPdvK8/W1XhtI3w/savvJ0rE0haQbSDPQl+RN\nM4Gj6x6sSfoMcGPJVduSNuzg7sRSJ+lO4NXAbbEwpxkkbWn7XqWmH8PYvqPumMbSjwsRJe1u+8cF\n379R55EmGG12vqXPZ+knrYprhaS9bP9wgu8/tPjCz2xvN5HXaoKq99eeSO1o8yhlWsU22TGkHOkv\nknacW0krfuv2fuCjkp4j3SZvLZqqs3TO4nwwSZfZPrTG9x7J8znXjBzTNLo0F7+HfIg08/P5EZ4z\naYV3KKBtsdSRkmYOfb7GahlNO480wWmlA+hxE75WSPqw7c8Dhw5JiQLA9ogNhoZYf0i1mnXaH3dh\npZpK99deG0g/ANwk6WoGdzbs205TOdeyCbMBTSjp1r40d9NiUQy4WVIrH3c/UuWZKwvH1Ndai19t\n7106lnHqympCE3RP/vOnRaNo3nmkuEhnWeomc634Zf7z7km8/z8MedzVKR1V76+9ltoxYsepfi6g\nn8tCHcfwahm1D64lHQFsavvTuZrI2nXmWDWtNq9Sq/Rjgf1JF+drgXPcSwdll8qLlg9g+HHTqA/l\nkraxPZkLZOhQ084jTSDpv2wfLukuBs+U9m25xip1y7VCXdKFter9tacG0i2SVgKw/f9Kx1KapJ8B\n5wJ3MbAAs8TCujOAZUhF5F8laXXgWtfYWlTSi8Cz5KoswHOtp0gHz/S6YmmLaS3Smz9e93uH0Un6\nDqkD6NDjpu428gsYfgv3adKs7IdtP1BnPE0gaWfSSvuNGPwhp6727Y07j5QmaR3bj+XKEsOMtwpN\nGN1krxV53cdJDD9uKvsg2C0fLKveX3sqtUPSNsBF5JaVkp4A3mX7f4oGVtafbY/Ziasme9jeUdI8\ngLz6d9k6A7A9tc73G41SotvJwN+T2qW3Ls6zbZ9SMraw2PoNmUX7EvArUglLkWrBbgbcAfwnqUFD\nv7mYdKt50IecujTlPNIkth/Lfz6s1Mr91aQPgHNcY/faXlPxteIS0kC6yHHTJFXvr1Mqjq+0s4AP\n2d7I9kbAh4GzC8dU2umSTpa0u6QdW18F4liYb08ZQNIa9O/BfCKpQc4utlfPCy53BfaUdGLZ0EL2\nXUn7lw4COMj2mbYX2H7G9lnAX9n+OqmVeT963PYVth+0/XDrq3RQAST9NXA7qdzaYcBPJB1TNqqu\nVuW14gnb37T9C9u/bH1VHnEXqWp/7anUjpFKsnR7mZbJymXn3klacNBeW7vW6gOS3gUcDOxMmkk7\nHJhl+9I642iCPCu/n+0nhmxfC7jOfVIXuMly7dX/S5psWEih2/aSfkyquPPfedNhpMmC3ZpQm70E\nSfuSynjewOBF5d8sFlQAQNJ9pLuPT+bHawC32t6ibGTdqcprRZ4YOIThx80Vo/5Sh7qtrn1V+2tP\npXYAD0j6J1J6B6Qufn2XQzjEDNICvxdKvLmkabYX2b5Q0lzgDaRByYw+XiS1zNATI6TcN0nLlAgo\nDPMFYHfgrsILet4BnA78O+luzk+Ao5Q6m/19wbhKOhrYkrTmYvHkABAD6fKeJDX6aFmQt4WJqfJa\n8Q5gW2BlBh834x5IS5ph+xtjbDu9w5hKq2R/7bWB9DHALAZOqLfkbf3sbmBV4PeF3v92cs3VnKve\nz/nqLWN9qCnygScM8yhwd+lV8Xkx4VtGeXpCzRV6wC4xw9ksklq1iO8HbpP0bdIg7a3A/GKBdb8q\nrxW7VXDcnAR8Y7Rtts+f5OvXour9tacG0rb/CHRbYfClbVXgXklzGHw7p67yd/1U53a8tpP0zAjb\nBSxfdzBhRK2a9N+lYE36fAv3bxhehq+fJwhulbSV7Z+XDiQstnL+85cM1C0G+HaBWHpJldeK2yRt\nYfu+ToOQ9CZSi/H1hjRmmQ4s6vT1GqDS/bUnBtKSxrw1UaJmcoOMWFu7Rmu1ffobpml1eesQq/67\nwoP5a9n8Vcq3SXfWvge8WDCOJtkNuFPSg6QPOVGruLChZSGjBG01Kr5W7ADMl3Q/g4+b8RQf+A2p\n5OZBDG7GsoC0ILKrVL2/9sRiQ0mPk27FXgLcxpBZ0LprJjeZpL2AmbY/UNP7PQb8B6PMTNddlzeE\nTkiaTrrYLFjiDy+d9+/LBYVjiVrFzTW0BC0QJWgbQtJmI23vpHKHpGVsL8zfrwZsYLtrU3eq2l97\nZSA9FdiPtJJ7W+Bq4JI4eBNJOwBHkhYePghcZvuMmt67Kwq0h9AuN/04j4FbgE8Dx7jGTpw5jlNJ\nq8i/U+f7Nl0u4bkXKa/xR7bvKBxSACTdCnzc9o358euBT9veo2hgAQBJ2zL4uOloECzpJtKs9DTS\nzPTvSeenrpuVhur2156oI237RdvX2H436bbf/aT8xn5d1Y6kzXP96HuB2cAjpA9Oe9c1iG6FMq4f\nSp9uQ2iK/wT+zvbGtjcGPkAaWNfteOAqSX+S9IykBaPkTPYNSf8MXACsAawJnCfpE2WjCtnLWoMS\nANs3AS8rF05okfRx0l379YD1ga9JOqnDl1nF9jOkMnoX2t4V2LfaSGtVyf7aEzPSAJKWAw4gzUpv\nTCrp8p+2f10yrlIkvUTKrTzW9v152wO2N605jtVt/2EcPxcz16ExRqqHGvtoM+Tar9vZ/nN+vAJw\nZ1TyKE/S5aSum+0laHeyfXC5qAIsPm52sP1cfrwiMK+T40bSXcD+pA+yH7c9R9L8bl2fUNX+2iuL\nDS8EtgG+Q2ry0a/1idsdQmonfKOka4BLKVBBYzyD6Cyqe4Ti2rp+3izpTNIMjoG3AzfVGMeWtu8d\nrQtpn6cy/IZUseDP+fFyQF9OmDRQlKBtrscYPOablrd14hTgWlJayBxJmwK/qCi+EirZX3tiRjrP\nvj6bH7b/hYp0I2sSSS8j1UacCewDXAhcbvu6ooENEbN9oQkk3TjG07V1BJV0lu33jhJP7Z1Jm0DS\nbNL5fUNgF+D6/Hg/4HbbhxQML4RGkvRF0nGyMem4uTY/3h+YY/uwctH1hp4YSIfxyXnIM4C32963\ntS3X3y4qBtIhhLFIevdYz9u+oK5YwmBRgra5JB071vO2z+3gtTYnVeFa2/Y2efHiQbZPnWSYtap6\nf42BdJ9rygB2pJzUEErJC9qGsX1KzXHMAK6xvSAvqNsR+Bfb8+qMI4SxRAna/iDpZuAfgDNb12tJ\nd9vepmxknal6f+2JHOkwKbXlJucyhWszuEPbI/nbbl75G3rPs23fLw8cCNxTII5/sv2NXP/9DcDn\ngK8CuxaIpRFyI5ZhM0B1L6QOg7yCgRK0RxIlaBtH0i8Y+bjZvIOXWdH27dKgYUM3djasdH+NgXSo\n5ZaEpONIXRZ/B7zU9t7bQkeLEkNY6mx/vv2xpNNIuYV1a3UzPAA4y/bVubZ0P9u57fvlSelqq4/y\ns6EGtl8ErgGuyRW0ZpJK0M6qudxqGN1ebd+3jptVOnyNJ3JjFwNIOozOFywWV/X+Gqkdfa6u1I7c\nlnRX208u7fcKoWp5fcEc26+s+X2vIlWk2I+U1vEn0sK67eqMo+kkzbW9U+k4+lmUoO0+kn5qe+cl\n/+Tin98UOAvYA/gjqcHbO7qxq2iV+2vMSIe6UjseJXWHC6Hxcr3U1izDVGAtUumnuh0OvBE4zfZT\nktYh5Sj2rSElAaeQZqjjWlZQlKBtvrwwsKV13CzXwe9PAXa2/YZcDWyK7QUVh1mLqvfXmJHuA2Pl\nJo+3YUoFMZwLbEHKRXq+LY4vLO33DqFTkjZqe7gI+J3t2nMB823UX9l+Prev3ZbUUeypumNpiiEl\nARcBD5E+aNxXJqIQJWibT9ItbQ9bx83nbP+8g9foaAa7qareX2Mg3eNGy02uuxORpJNH2m57Vp1x\nhDCW3O1roe2F+fEWwJuBh2xfXiCeO0kzRxuTZk++DWxt+811xxJC6G+S/hV4Avg6bQuy+32NUwyk\ne1zkJocwfpJ+ABxr+xeSXgncDlwMbEXKkf7HmuO5w/aOkj4C/Mn27H4tFSnpLcD8Vj5mLlF4KPAw\ncLztB0vGF0ITSXozcHfbXeiPMXDcnNhJfnOumDOU+71iTuSV9b5G5CZLWgv4CLA1acUwAP3YoS00\n2mq2Wy1v300qiXScpGWBuUCtA2lgoaSZwLuAt+Rty9QcQ1N8CtgNQNKBwFGkhUI7kEoC/lW50EJo\nrM+QFgci6QBSC+x3kI6bM0lrMMbF9iZLI8BuN6V0AGGpe4BU1uUkSR9qfRWI42LgXmATUm/7Raym\nkwAADnVJREFUh4A5BeIIYSztt+j2IbWhxvYLDKRG1eloYHfgU7YflLQJcFGBOJrAtp/L3x8CnGt7\nru1zSItBQwjD2XYrDeMQ4Bzbt9n+Kmnt1LhJWlHSJySdlR//Rf5Q29diIN37HiENBpYFVm77qtsa\nuRXpQts32z6GNFAJoUnmSzpN0onAK4HrACStWiKYvBDoo8Ad+fGDtj9bIpYGkKSVcvWAfYEb2p5b\nfpTfCaHfTckDYJGOm++3PTfuqh3ZecAL5BluUmnOfq9rH6kdva5Bi/kW5j8fy7eXfkM0UQjN8zfA\n8aTFffu3zYBuBZxWdzA5L/g00gfhTSRtD5xi+6C6Y2mALwF3As8A99j+KYCkHejCphAh1GQ2MI+U\n4vkL27cDSNoO+G2Hr7WZ7bfndDNsP6chbQ77USw27HFNyU3Ot39uATYgHdjTSfUbr6gzjhCqIOky\n24fW8D5zSXdubmotMJR0t+1tlvZ7N5Gk9YCXAz+z/VLetg6wTNtiqq2jNXUIAyRtSErjuCN39Wsd\nS8vYfig/3tL2vUt4nVtJs9o/yougNyOtI3n1Uv0LNFzMSPe+i0mlag4E3k9aQPV43UHYvip/+zSw\nd93vH0LF6lqlvtD200MmfUrkajdC7jr26yHbhs5GX0TqAhlCYHHfiEeGbBvawe9rLPm4+SSptfYG\nki4G9iSt4+hrkSPd+xqRmyxpfUmXS3pc0u8lXSZp/brjCKEidd3K+x9JRwJT88Ke2cCtNb13t+r7\nW80hTMASjxvb15EWLL4HuITU6fDGMX+pD8RAuvcNyk3O+YQlcpPPI/WyXwdYF7gybwshjO44UlrW\n86QZo6eBE4pG1HyRrxhC55Z43Ei6wfaTtq+2fZXtJyTdsKTf63WR2tH7TpW0CvBhBnKTTywQx1q2\n2wfO50uKAUHoVrXMeubFjh/PXyGEUDtJywMrAmtKWo2B8990YL1igTVEDKR7XINyk5+UdBTpdhCk\nRgrRbTF0q4/W8SaSrgdm2H4qP14NuNR2NB8Z3QulAwihC704xnPvI90JW5fUmKo1kH4GOGMpx9V4\nUbWjx+U85NnAXqRbN7eQ2un+quY4Nspx7J7juBU4zvajdcYRwnhI2pO0sGYj0oSDKNAKd6R24P3a\nIrwl317ed0nbQgiDSTqCVMLuU5I2AF5ue24Hv3+c7dlLL8LuFDPSve8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112994210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb3 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=4,\n",
    "        min_child_weight=6,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        reg_alpha=0.005,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb3, train, test, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until cv error hasn't decreased in 50 rounds.\n",
      "Stopping. Best iteration:\n",
      "[1732] cv-mean:0.8452782\tcv-std:0.0126670016879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.885261\n",
      "AUC Score (Test): 0.849430\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z2FWqk6OkdwBvI/UWvDoiruh1Yt1yJ0czMzMz67Wq42ATEVOBrwC/BR7qYMErSbpJ0u2S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\nNylGbc9kp9li4d+CnljV9vckfQTA9mJJpcOZTZbut63LhY/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urEvXCBAhZ4PJuIvxfbql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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11463b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb4 = XGBClassifier(\n",
    "        learning_rate =0.01,\n",
    "        n_estimators=5000,\n",
    "        max_depth=4,\n",
    "        min_child_weight=6,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        reg_alpha=0.005,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb4, train, test, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
